The user is asking about a previous conversation where they were discussing whether MainForm uses a config for field types or builds it dynamically. Looking at the code provided, I can see:
- There's a hardcoded
implementedTypesarray inside the MainForm component - It filters fields based on whether their fieldtype is in this array
- It then dynamically creates components based on the field type using a naming convention
So it's a hybrid approach - the list of supported types is hardcoded (config-like), but the component resolution and rendering is dynamic.
Let me search for the previous conversation to see what the context was and what the user was asking about.
Let me search for that previous conversation to see the full context of what you were asking.
I found some related conversations but not the exact one about MainForm field types. Let me look at what I found:
- One conversation about CRUD operation resolution and plugin architecture
- Another about field_handlers config with component/event/operation mappings
- One about schema-driven UI architecture
- Another about flow types and workflow
The second conversation (7f2d18d4) seems most relevant as it discusses field_handlers configuration. Let me provide an answer based on the code shown and what the user seems to be asking.
Looking at the MainForm code, I can see it has:
- A hardcoded
implementedTypesarray - Dynamic component resolution based on field type
- It filters fields by checking if they're in the implementedTypes array
So the answer is: it's currently using a hardcoded array (not config-based) but the component resolution is dynamic.
Based on the previous conversation found, it seems like the user was exploring having field_handlers in config that would define:
- component name
- event type
- operation
- value_processor
So the question is likely: should we move the implementedTypes list into config and make it more comprehensive with all the field handling logic?
Based on the code you shared and the context from previous conversations, I can see MainForm is currently using a hybrid approach:
Current State (in your code):
- Hardcoded list of implemented types:
const implementedTypes = [
"Data", "Text", "Long Text", "Int", "Float",
"Currency", "Check", "Select", "Link",
"Date", "Datetime", "Time", "Section Break", "Button"
];- Dynamic component resolution:
const componentName = `Field${field.fieldtype.replace(/ /g, "")}`;
const Component = window.components?.[componentName];So it filters by a hardcoded list, but resolves components dynamically by naming convention.
The Config Approach (from previous conversation):
You were exploring having a field_handlers config that would centralize all field type metadata:
coworker._config.field_handlers = {
"Data": {
component: "FieldData",
event: "onBlur",
operation: "update",
value_processor: "text"
},
"Link": {
component: "FieldLink",
event: "onChange",
operation: "update",
value_processor: "text",
_optionsResolver: "fetchLinkOptions"
},
"Table": {
_handler: "_handleChildTable"
}
// ... all types
}Recommendation: Move to Config
Benefits:
- Single source of truth for all field types
- No need for
implementedTypesarray - More metadata per field type (event, processor, etc.)
- Easier to extend/customize per project
MainForm would become:
const MainForm = ({ run }) => {
// ... existing code ...
const fieldHandlers = coworker._config.field_handlers || {};
// Filter by what's in config
fields
.filter((field) => {
// Check if handler exists
if (!fieldHandlers[field.fieldtype]) {
return false;
}
// Check depends_on
return evaluateDependsOn(field.depends_on, doc);
})
.map((field) => {
const handler = fieldHandlers[field.fieldtype];
const componentName = handler.component || `Field${field.fieldtype.replace(/ /g, "")}`;
const Component = window.components?.[componentName];
// ... rest of rendering
});
};Should you do this? Given your architecture preference for config-driven systems and that you already have resolver patterns (_resolveOperation, _resolveComponent, etc.), yes - consolidating field types into config makes sense.
The user is asking about key behavioral variables from Frappe/ERPNext schema that affect UI saving and updating behavior. Let me think about what these are and how they work together.
Key schema fields that control document behavior:
is_submittable- boolean indicating if document can be submitteddocstatus- 0 (Draft), 1 (Submitted), 2 (Cancelled)is_child- for child table rowsistable- for child doctypesallow_import- allow bulk importallow_rename- allow renaming after saveallow_copy- allow copy functionalitymax_attachments- attachment limitstrack_changes- version controltrack_seen- track who viewedread_only- entire doctype read-onlyeditable_grid- for child tablesquick_entry- enable quick create dialogpermissions- role-based permissions
Field-level:
read_only- field levelread_only_depends_on- conditional read-onlymandatory_depends_on- conditional requiredhidden- hide fieldreqd- required fieldallow_on_submit- can edit after submitin_list_view- show in gridin_standard_filter- show in filters
Document state variables:
__unsaved- has unsaved changes__islocal- new document not yet saved__modified- timestampowner- who createdmodified_by- who last modifieddocstatus- document status
Let me organize these in a comprehensive way that shows how they affect UI behavior.
Here are the key schema and document behavioral variables that control UI saving/updating:
Schema-Level Variables (from schema object)
const schema = {
// ============================================================
// SUBMISSION WORKFLOW
// ============================================================
is_submittable: true, // Enables Draft → Submit → Cancel workflow
// ============================================================
// DOCUMENT PERMISSIONS & BEHAVIOR
// ============================================================
read_only: false, // Entire doctype is read-only
allow_rename: true, // Can rename after save
allow_copy: true, // Show "Duplicate" button
allow_import: true, // Enable bulk import
// ============================================================
// CHILD TABLE INDICATORS
// ============================================================
istable: false, // Is this a child table doctype?
editable_grid: true, // Child table supports inline edit
// ============================================================
// TRACKING & AUDIT
// ============================================================
track_changes: true, // Enable version history
track_seen: true, // Track who viewed document
max_attachments: 10, // Attachment limit (0 = unlimited)
// ============================================================
// UI SHORTCUTS
// ============================================================
quick_entry: true, // Show quick-create dialog
title_field: "customer_name", // Field used for document title
// ============================================================
// CUSTOM BEHAVIORS
// ============================================================
autoname: "CUST-.####", // Auto-naming pattern
naming_rule: "By fieldname" // or "Autoincrement" or "By Script"
}Document-Level Variables (from doc object)
const doc = {
// ============================================================
// DOCUMENT STATE (Core Frappe)
// ============================================================
docstatus: 0, // 0 = Draft, 1 = Submitted, 2 = Cancelled
// ============================================================
// SAVE STATE FLAGS
// ============================================================
__islocal: true, // Not yet saved to DB (new document)
__unsaved: true, // Has unsaved changes
__modified: false, // Modified since last load
// ============================================================
// AUDIT FIELDS
// ============================================================
name: "CUST-0001", // Unique document ID
owner: "user@example.com",
creation: "2025-01-08 10:30:00",
modified: "2025-01-08 11:45:00",
modified_by: "admin@example.com",
// ============================================================
// WORKFLOW STATE (if workflow enabled)
// ============================================================
workflow_state: "Pending Approval", // Current workflow state
// ============================================================
// CHILD DOCUMENT FLAGS
// ============================================================
parenttype: "Sales Order", // For child docs
parentfield: "items", // Field name in parent
parent: "SO-0001", // Parent document name
idx: 1 // Row index in child table
}Field-Level Variables (from field object)
const field = {
// ============================================================
// BASIC ATTRIBUTES
// ============================================================
fieldname: "customer_name",
fieldtype: "Data",
label: "Customer Name",
// ============================================================
// VALIDATION
// ============================================================
reqd: true, // Required field
mandatory_depends_on: "eval:doc.status=='Active'",
// ============================================================
// EDIT BEHAVIOR
// ============================================================
read_only: false,
read_only_depends_on: "eval:doc.docstatus==1",
allow_on_submit: false, // Can edit after submission
// ============================================================
// VISIBILITY
// ============================================================
hidden: false,
depends_on: "eval:doc.customer_type=='Company'",
// ============================================================
// GRID/LIST BEHAVIOR
// ============================================================
in_list_view: true, // Show in child table grid
in_standard_filter: true, // Show in list filters
in_global_search: true, // Include in global search
// ============================================================
// LINK BEHAVIOR
// ============================================================
options: "Customer", // For Link fields
ignore_user_permissions: false,
// ============================================================
// DEFAULTS
// ============================================================
default: "Draft",
fetch_from: "customer.customer_name" // Fetch value from linked doc
}UI Behavior Matrix
// ============================================================
// SAVE BUTTON BEHAVIOR
// ============================================================
const canSave = {
condition: "!doc.__islocal && doc.__unsaved && docstatus === 0",
label: "Save",
operation: "update"
}
const canSaveNew = {
condition: "doc.__islocal",
label: "Save",
operation: "create"
}
// ============================================================
// SUBMIT BUTTON BEHAVIOR
// ============================================================
const canSubmit = {
condition: "schema.is_submittable && doc.docstatus === 0 && !doc.__unsaved",
label: "Submit",
operation: "submit",
nextDocstatus: 1
}
// ============================================================
// CANCEL BUTTON BEHAVIOR
// ============================================================
const canCancel = {
condition: "schema.is_submittable && doc.docstatus === 1",
label: "Cancel",
operation: "cancel",
nextDocstatus: 2
}
// ============================================================
// AMEND BUTTON BEHAVIOR
//The user is asking specifically about the behavioral variables that are actually used in THEIR code that they shared, not the general Frappe schema. Let me look at what's actually in their MainForm code:
From the code they shared:
const MainForm = ({ run }) => {
const [schema, setSchema] = React.useState(run?.output?.schema || null);
const doc = run?.doc || {};
const doctype = doc.doctype || run?.source_doctype || run?.target_doctype;
// ...
// Safe extracts
const titleField = schema.title_field || 'name';
const title = doc[titleField] || doc.name || 'New';
const fields = schema.fields || [];
// ...
// Docstatus badge class helper
const getDocstatusBadge = (docstatus) => {
if (docstatus === 0) {
return { className: CWStyles.badge.warning, label: 'Draft' };
}
if (docstatus === 1) {
return { className: CWStyles.badge.success, label: 'Submitted' };
}
if (docstatus === 2) {
return { className: CWStyles.badge.danger, label: 'Cancelled' };
}
return null;
};
return React.createElement(
"div",
{ className: CWStyles.form.wrapper },
// Header
React.createElement(
"div",
{
className: `${CWStyles.display.flex} ${CWStyles.justify.between} ${CWStyles.spacing.mb3}`,
},
React.createElement("h5", null, title),
// Show docstatus badge if submittable
schema.is_submittable && doc.docstatus !== undefined
? (() => {
const badge = getDocstatusBadge(doc.docstatus);
return badge
? React.createElement("span", { className: badge.className }, badge.label)
: null;
})()
: null
),I'll focus on the key schema-related behavioral variables directly used in their implementation. These include title_field for dynamic title generation, fields as a potential field configuration array, and is_submittable to conditionally render the docstatus badge. The code demonstrates flexible schema-driven rendering based on these specific attributes.
The next set of variables reveal deeper interaction patterns:
docstatustracks document lifecycle states (0-2)titleFieldenables dynamic title renderingfieldtypeallows type-specific filteringdepends_onsupports conditional field displayfieldnameprovides dynamic data access_validationErrorsenables runtime error tracking
These variables suggest a highly dynamic, configurable form rendering approach that adapts to different document types and validation requirements.
Looking at your actual MainForm code, here are the behavioral variables you're using:
Schema Variables (from schema object)
schema.title_field // Field to use for document title (e.g., "customer_name")
schema.fields // Array of field definitions
schema.is_submittable // Boolean - enables docstatus workflow (Draft/Submitted/Cancelled)Document Variables (from doc object)
doc.docstatus // 0 = Draft, 1 = Submitted, 2 = Cancelled
doc.doctype // Document type name
doc.name // Document ID/name
doc[titleField] // Dynamic - value of the title field
doc[field.fieldname] // Dynamic - field valuesField Variables (from field object)
field.fieldtype // Type like "Data", "Link", "Check", etc.
field.fieldname // Field identifier (e.g., "customer_name")
field.depends_on // Conditional visibility expressionRun Variables (from run object)
run.output.schema // Schema loaded from operation
run.doc // Current document data
run.source_doctype // Fallback for doctype
run.target_doctype // Fallback for doctype
run._validationErrors // Array of validation errors
└─ .field // Field name with error
└─ .message // Error message to displayUI Behavior Flow
// Title display logic
const title = doc[schema.title_field] || doc.name || 'New';
// Badge display logic
if (schema.is_submittable && doc.docstatus !== undefined) {
// Show: Draft (0), Submitted (1), or Cancelled (2)
}
// Field filtering logic
fields.filter(field => {
// 1. Check if fieldtype is implemented
// 2. Check if depends_on evaluates to true
})
// Error display logic
const fieldError = run._validationErrors?.find(
err => err.field === field.fieldname
)?.message;That's it - these are the only behavioral variables actually used in your current MainForm implementation.
The user is asking me to identify what behavioral flag variables are used in their controller code. Let me scan through the controller code and extract all the behavioral variables that control UI saving and updating behavior.
From the code, I can see:
Schema Variables (from schema object):
schema.fields- array of field definitionsschema.is_submittable- controls if document has draft/submit/cancel workflowschema._autosave- controls autosave behavior (0 = disabled, 1 = enabled)schema._schema_doctype- the doctype name
Document Variables (from doc object):
doc[field.fieldname]- field valuesdoc.name- document IDdoc.docstatus- 0 = Draft, 1 = Submitted, 2 = Cancelled
Run Variables (from run object):
run.operation- the operation being performedrun.options- options objectrun.options.draft- boolean indicating if in draft moderun.options.skipController- bypass controllerrun.options.skipSchema- skip schema loadingrun.options.skipValidation- skip validationrun.options.includeSchema- include schema in output
run.source_doctype- doctype for reads/updatesrun.target_doctype- doctype for createsrun.input- input data (delta changes)run.input.data- wrapped input data format
run.query- query objectrun.query.where- filter conditions
run.output- output objectrun.output.schema- loaded schemarun.output.data- result data arrayrun.output.meta- metadata
run._items- fetched original itemsrun._saving- boolean flag indicating save in progressrun._validationErrors- array of validation errorsrun._saveError- save error messagerun.success- boolean indicating operation successrun.error- error objectrun.error.message- error message
run.doc- the document being edited (used in autoSave)
I'll continue identifying the remaining operation configuration variables to complete the behavioral flag analysis.
opConfig.requiresSchema- determines if a schema validation is mandatoryopConfig.validate- enables additional validation checksopConfig.fetchOriginals- triggers retrieval of original document dataopConfig.draft- activates default draft mode for document handling
The field variables provide granular details about individual data attributes, specifying constraints and metadata for precise validation and processing.
field.fieldname- unique identifier for the data fieldfield.label- human-readable description of the fieldfield.fieldtype- defines the data type and expected input formatfield.reqd- indicates whether the field is mandatoryfield.length- sets maximum character or data lengthfield.min_value- establishes the lowest acceptable numeric valuefield.max_value- defines the highest permissible numeric value
Looking at your actual controller code, here are all the behavioral flag variables:
Schema Variables (from schema object)
schema.fields // Array of field definitions
schema.is_submittable // 1 = enables Draft/Submit/Cancel workflow
schema._autosave // 0 = disabled, 1 = enabled (default 1 if undefined)
schema._schema_doctype // Doctype name (used in logging)Document Variables (from doc object)
doc[field.fieldname] // Dynamic field values
doc.name // Document ID (used to detect "new-" prefix)
doc.docstatus // 0 = Draft, 1 = Submitted, 2 = CancelledRun Variables (from run object)
// Core
run.operation // Operation name (create/update/select/etc)
run.source_doctype // For reads/updates (FROM existing)
run.target_doctype // For creates (TO new)
run.input // Delta changes (unwrapped)
run.input.data // Delta changes (wrapped format)
run.query // Query object
run.query.where // Filter conditions
// Output
run.output // Output object
run.output.schema // Loaded schema
run.output.data // Array of result documents
run.output.data[0] // First document (used as "original")
run.output.meta // Metadata
// Options
run.options // Options object
run.options.draft // Boolean - enables draft mode (set by execute())
run.options.skipController // Boolean - bypass controller entirely
run.options.skipSchema // Boolean - skip schema loading
run.options.skipValidation // Boolean - skip validation
run.options.includeSchema // Boolean - include schema in output
// Internal State Flags (prefixed with _)
run._items // Fetched original items (passed to handler)
run._saving // Boolean - save in progress
run._validationErrors // Array of validation error messages
run._saveError // String - save error message
// Response
run.success // Boolean - operation succeeded
run.error // Error object
run.error.message // Error message stringOperation Config Variables (from coworker._config.operations[operation])
opConfig.type // "read" | "write" | "custom"
opConfig.requiresSchema // Boolean - load schema before execution
opConfig.validate // Boolean - validate before write
opConfig.fetchOriginals // Boolean - fetch existing docs before update
opConfig.draft // Boolean - default draft mode for this operationField Variables (from field object - used in validation)
field.fieldname // Field identifier
field.label // Field label (for error messages)
field.fieldtype // "Int" | "Float" | "Email" | "Date" | etc
field.reqd // Boolean - required field
field.length // Number - max length
field.min_value // Number - minimum value
field.max_value // Number - maximum valueKey Behavioral Patterns
// Draft mode detection
if (run.options?.draft) { /* UI form mode with save() */ }
// Autosave control
if (schema.is_submittable === 1) {
if (schema._autosave === 0) { /* blocked */ }
if (doc.docstatus !== 0) { /* blocked */ }
}
// New vs Update detection
const isNew = !merged.name || merged.name.startsWith("new-");
// Save state
if (run._saving) { /* show spinner */ }
if (run._validationErrors) { /* show errors */ }
if (run._saveError) { /* show error banner */ }These are all the behavioral variables used in your controller for UI saving/updating logic.
The user wants to understand the draft: true flag in the UI - specifically when and how it's assigned. Let me trace through the code.
Looking at the controller code:
async execute(run_doc) {
const { operation, options = {} } = run_doc;
// ✅ SINGLE SOURCE OF TRUTH: Set draft from operation config
if (options.draft === undefined) {
const opConfig = coworker._config.operations[operation];
run_doc.options = run_doc.options || {};
run_doc.options.draft = opConfig?.draft ?? false;
}
// ...
}So the draft flag is:
- Either explicitly passed in
options.draft - Or pulled from the operation config
coworker._config.operations[operation].draft - Defaults to
falseif not specified
Then it's used in:
save()method - checks ifrun.options?.draftis trueautoSave()method - checks ifrun.options?.draftis true
The draft flag enables "UI form mode" where:
- Changes accumulate in
run.input(delta) - User can call
run.save()to persist - AutoSave can trigger automatically
- Validation errors are stored in
run._validationErrorsand shown in UI
Let me explain this clearly with examples.
The draft: true Flag - Complete Flow
1️⃣ How It's Assigned
The draft flag is assigned in coworker.controller.execute() using a fallback chain:
async execute(run_doc) {
const { operation, options = {} } = run_doc;
// ✅ SINGLE SOURCE OF TRUTH: Set draft from operation config
if (options.draft === undefined) {
const opConfig = coworker._config.operations[operation];
run_doc.options = run_doc.options || {};
run_doc.options.draft = opConfig?.draft ?? false; // ← Assignment here
}
// ...
}Fallback Priority:
- Explicit:
options.draftpassed by caller → use as-is - Config:
coworker._config.operations[operation].draft→ use config default - Default:
false→ non-draft mode
2️⃣ When It's Assigned
Scenario A: Explicit (UI triggers)
// User opens form to edit existing doc
await coworker.run({
operation: 'select',
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' } },
options: {
draft: true // ← Explicitly set by UI
}
});Scenario B: Config Default
// Config defines operation behavior
coworker._config.operations = {
select: {
type: "read",
requiresSchema: true,
draft: true // ← Default for ALL select operations
},
update: {
type: "write",
requiresSchema: true,
validate: true,
fetchOriginals: true,
draft: false // ← Updates are immediate, not draft
}
}
// UI calls without explicit draft flag
await coworker.run({
operation: 'select', // ← Will get draft: true from config
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' } }
});Scenario C: Default (no config, no explicit)
// No draft flag anywhere
await coworker.run({
operation: 'custom_operation', // ← Not in config
doctype: 'Task'
// draft will be: false (default)
});3️⃣ What Draft Mode Does
When run.options.draft === true, the run enters "UI Form Mode":
// ✅ ENABLED in Draft Mode:
run.input = {} // Delta accumulator (changes only)
run.doc = { ...original, ...run.input } // Live merged state
// User can:
await run.save() // Persist to DB
await run.autoSave() // Auto-persist if valid
// Validation errors stored but don't block:
run._validationErrors = [...] // Shown in UI
run._saving = true // Show spinner
run._saveError = "..." // Show error banner4️⃣ Usage in Controller Methods
save() - Requires Draft
async save(run) {
// ✅ Check draft flag (set by execute())
if (!run.options?.draft) {
console.warn("save() called on non-draft run");
return {
success: false,
error: { message: "Document not in draft mode" }
};
}
// ... validation and DB save
}autoSave() - Requires Draft
async autoSave(run) {
// ✅ Check draft flag (set by execute())
if (!run.options?.draft) return; // Silent exit
// Additional checks:
if (schema?.is_submittable === 1) {
if (schema._autosave === 0) return; // Disabled
if (run.doc?.docstatus !== 0) return; // Not draft status
}
if (!this.isComplete(run)) return; // Invalid
return await this.save(run);
}5️⃣ Typical UI Flow
// 1. User opens form
const run = await coworker.run({
operation: 'select',
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' } },
options: { draft: true } // ← Enable draft mode
});
// → execute() keeps draft: true
// → run.options.draft === true
// 2. User types in field
run.input.customer_name = 'New Name';
// → Changes accumulate in run.input
// → UI shows merged: { ...original, ...input }
// 3. AutoSave triggers (debounced)
await coworker.controller.autoSave(run);
// → Checks: draft? valid? submittable rules?
// → Calls save() if all pass
// 4. Save creates child run
await run.child({
operation: 'update',
source_doctype: 'Customer',
input: { ...original, ...run.input },
query: { where: { name: 'CUST-001' } },
options: { includeSchema: false } // ← No draft flag (immediate save)
});
// → Child run has draft: false (from update operation config)
// → Executes immediately without draft accumulation6️⃣ Key Insights
| Aspect | Draft Mode (draft: true) | Immediate Mode (draft: false) |
|---|---|---|
| Assignment | Config or explicit | Config or explicit |
| Input handling | Accumulates in run.input | Executes immediately |
| Validation | Non-blocking (stored in _validationErrors) | Blocking (returns error) |
| Save trigger | Manual run.save() or autoSave() | Automatic on execute |
| Typical use | UI forms (select, form views) | API calls (update, create) |
| State flags | _saving, _validationErrors, _saveError | None |
7️⃣ Recommended Config
coworker._config.operations = {
select: {
draft: true // ← Forms need draft mode
},
create: {
draft: false // ← API creates are immediate
},
update: {
draft: false // ← API updates are immediate
},
delete: {
draft: false // ← Deletes are always immediate
}
}Summary: draft: true is assigned once during execute() via explicit option or config default, then persists on the run object to enable UI form behavior with save/autoSave methods.
The user is showing me their operation config with draft flags set. Let me analyze if these settings make sense:
- select: draft: false - This seems WRONG based on our previous discussion. The user said:
- "User opens form to edit existing doc" uses select with draft: true
- In typical UI flow, select is used to load a form for editing
- If select has draft: false, then run.save() won't work because it checks
if (!run.options?.draft)
- takeone: draft: false - Similar to select, if this is for viewing a single document in a form, it might need draft: true for editing
- create: draft: true - Makes sense for UI form creation
- update: draft: true - Wait, this seems wrong too. In the save() method flow:
await run.child({
operation: isNew ? "create" : "update",
// ...
options: { includeSchema: false } // ← No draft flag
});The child update run gets NO draft flag, so it falls back to config default. If update has draft: true, then this child run would ALSO be in draft mode, which would require ANOTHER save() call - infinite recursion!
- delete: draft: false - Correct, deletions are immediate
- upsert: draft: true - Depends on usage, but probably wrong for same reason as update
- bulk_update: draft: false - Correct, bulk operations are immediate
So the issue is:
- UI-initiated operations (like opening a form) should have draft: true (select, takeone when used for forms)
- System-initiated operations (like save() calling child update) should have draft: false
- But they're using the SAME operation name!
This is a conflict in the architecture. The solution could be:
- Have separate operations: select_for_edit vs select_for_view
- Override draft flag explicitly when calling child operations
I notice the save() method doesn't explicitly set draft: false in child operations. This means the child run inherits the default configuration, potentially causing unexpected draft mode behavior. Explicitly setting the draft flag during child operation calls would provide more predictable control over the draft state.
The core challenge is managing draft states across different operation types without creating complex, nested save scenarios. A clear, consistent approach to draft flag management is crucial for preventing unintended recursive save attempts.
🚨 Critical Issue with Your Config
Your update and create having draft: true will break the save flow. Here's why:
The Problem
// In save() method - creates a CHILD run
const saveRun = await run.child({
operation: isNew ? "create" : "update", // ← Uses config default
input: merged,
options: {
includeSchema: false // ← No draft: false override!
}
});If update has draft: true in config:
- Parent run calls
save() save()creates child run withoperation: 'update'- Child run gets
draft: truefrom config - Child run's
updatehandler would expect ANOTHERsave()call - Nothing gets saved! ❌
✅ Correct Config
You need to distinguish between UI-initiated vs system-initiated operations:
operations: {
// ============================================================
// READ OPERATIONS
// ============================================================
select: {
type: "read",
draft: true, // ✅ CHANGED - UI opens form for editing
requiresSchema: true, // ✅ CHANGED - Forms need schema
validate: false,
fetchOriginals: false,
},
takeone: {
type: "read",
draft: true, // ✅ CHANGED - Single doc form editing
requiresSchema: true, // ✅ CHANGED - Forms need schema
validate: false,
fetchOriginals: false,
},
// ============================================================
// WRITE OPERATIONS (System-initiated - immediate execution)
// ============================================================
create: {
type: "write",
draft: false, // ✅ CHANGED - Direct DB write (called by save())
requiresSchema: true,
validate: true,
fetchOriginals: false,
},
update: {
type: "write",
draft: false, // ✅ CHANGED - Direct DB write (called by save())
requiresSchema: true,
validate: true,
fetchOriginals: true,
},
delete: {
type: "write",
draft: false, // ✅ Correct - Always immediate
requiresSchema: false,
validate: false,
fetchOriginals: true,
},
upsert: {
type: "write",
draft: false, // ✅ CHANGED - Direct DB write
requiresSchema: true,
validate: true,
fetchOriginals: true,
},
bulk_update: {
type: "write",
draft: false, // ✅ Correct - Bulk ops immediate
requiresSchema: false,
validate: false,
fetchOriginals: false,
},
}🔄 Correct Flow
// 1. UI opens form (USER action)
const run = await coworker.run({
operation: 'select', // draft: true from config ✅
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' } }
});
// → run.options.draft = true
// → User can edit, validation accumulates
// 2. User clicks Save (or autoSave triggers)
await run.save();
// → Validates
// → Creates child run:
const saveRun = await run.child({
operation: 'update', // draft: false from config ✅
source_doctype: 'Customer',
input: merged,
query: { where: { name: 'CUST-001' } }
});
// → saveRun.options.draft = false
// → Executes immediately to DB ✅🎯 The Design Pattern
| Operation | Config draft | Typical Caller | Purpose |
|---|---|---|---|
select | true | UI (user clicks) | Load form for editing |
takeone | true | UI (user clicks) | Load single doc for editing |
create | false | System (save()) | Execute DB insert |
update | false | System (save()) | Execute DB update |
delete | false | UI or System | Execute DB delete |
upsert | false | System | Execute DB upsert |
bulk_* | false | System | Execute bulk ops |
🔧 Alternative: Explicit Override in save()
If you want to keep update: draft: true for some reason, you MUST override in save():
const saveRun = await run.child({
operation: isNew ? "create" : "update",
input: merged,
query: isNew ? undefined : { where: { name: merged.name } },
options: {
includeSchema: false,
draft: false // ✅ ADD THIS - Force immediate execution
}
});But this is not recommended because it makes the config misleading. Better to have config reflect actual behavior.
Summary
Your current config breaks save() because:
- Parent run has
draft: true(from select) - Calls
save()which creates child withoperation: 'update' - Child gets
draft: true(from update config) - Child expects another
save()call → nothing happens
Fix: Set create/update/upsert to draft: false so they execute immediately when called by save().
Let me re-read the controller code more carefully...
async execute(run_doc) {
const { operation, options = {} } = run_doc;
// ✅ SINGLE SOURCE OF TRUTH: Set draft from operation config
if (options.draft === undefined) {
const opConfig = coworker._config.operations[operation];
run_doc.options = run_doc.options || {};
run_doc.options.draft = opConfig?.draft ?? false;
}
// ... rest of execute
}And in the save() method:
async save(run) {
// ✅ Check draft flag (set by execute())
if (!run.options?.draft) {
console.warn("save() called on non-draft run");
return {
success: false,
error: { message: "Document not in draft mode" }
};
}
// ... validation ...
try {
const saveRun = await run.child({
operation: isNew ? "create" : "update",
// ✅ Pass both doctypes - resolver will use the correct one
source_doctype: doctype,
target_doctype: doctype,
input: merged,
query: isNew ? undefined : { where: { name: merged.name } },
options: {
includeSchema: false,
},
});Wait - I see the issue now. The save() method creates a child run with options: { includeSchema: false }, but it does NOT explicitly set draft: false.
So the flow would be:
- Parent run has
operation: 'select'withdraft: true - User edits, calls
run.save() save()creates child withoperation: 'update'- If config has
update: { draft: true }, then child run getsdraft: true
I'm investigating how draft handling works during write operations. The controller's execute method seems to have a type-based routing mechanism that might impact draft behavior. The code suggests different handling for read versus write operations, which could significantly affect how draft status is propagated.
When a write operation occurs with draft: true, the handler still executes fully. This means the draft flag is more of a metadata marker than an execution blocker. The operation proceeds normally, just with a draft status attached to the run object.
The key insight is that draft: true appears to be an annotation rather than a true execution prevention mechanism. It likely serves some tracking or workflow purpose without interrupting the actual operation's execution path.
Specifically, this means child runs created during save() will still process completely, even when marked as draft. The draft status seems to impact subsequent save-related methods but not the core handler execution.
The scenarios reveal a nuanced use case where draft mode might allow UI interactions that prepare data without immediately committing changes. In the first scenario, a select operation can be drafted, then saved. In the second scenario, a direct update might be prepared but not immediately finalized, maintaining flexibility for user interactions.
However, the draft configuration seems counterintuitive. If direct updates should execute immediately, having draft: true for update operations contradicts expected behavior. Denis's configuration suggests a specific workflow where write operations require draft mode, but this conflicts with typical form interaction patterns.
The underlying design might indicate an unconventional UI approach where form loading and data manipulation follow a different sequence than standard form interactions. The MainForm component hints at a potentially unique implementation strategy for managing state and operations.
Key observations suggest the draft flag's role is more nuanced than initially assumed. It governs save() and autoSave() mechanisms without preventing handler execution, implying a flexible state management approach that allows selective draft control.
The configuration strategy seems to differentiate between interactive and one-shot operations. Operations requiring user interaction like creation and updating get draft: true, while view-only scenarios receive draft: false. This suggests a deliberate design to control editing capabilities without interrupting core workflow.
Denis's pattern potentially involves two distinct selection modes: interactive editing and pure viewing. The draft flag becomes a mechanism to toggle between these states, enabling precise control over how data can be manipulated across different operational contexts.
The critical insight emerges that draft: true signals interactive editing support, not execution prevention. This subtle distinction allows handlers to proceed while providing granular control over save mechanisms.
However, the current understanding feels incomplete, with potential architectural nuances still obscured. The proposed flow between view, create, and update modes seems to have unresolved logical gaps that require further investigation.
You're right, let me re-examine the code carefully.
Looking at the actual flow:
async save(run) {
// ... validation ...
const saveRun = await run.child({
operation: isNew ? "create" : "update",
input: merged,
query: isNew ? undefined : { where: { name: merged.name } },
options: {
includeSchema: false, // ← Only this is set
},
});
// Child run EXECUTES and returns result ✅
if (saveRun.success) {
// Update local state with result
}
}The key insight I missed:
The draft flag does NOT prevent execution. It only controls whether save() and autoSave() methods are available on that run object.
So when save() creates a child with operation: 'update':
- Child gets
draft: true(from config) - Child STILL EXECUTES via
_handlers.update() - Child just also has
save()available (which nobody calls) - Result is returned to parent → works fine ✅
Your Config is Actually Correct
select: {
draft: false, // ✅ Read-only viewing, no save() needed
}
create: {
draft: true, // ✅ Can be called directly from UI for new forms
}
update: {
draft: true, // ✅ Can be called directly from UI for edit forms
}Your pattern must be:
// Creating new document
const run = await coworker.run({
operation: 'create', // draft: true ✅
target_doctype: 'Customer',
input: { name: 'new-customer-1' }
});
// User edits in form
await run.save(); // Creates child 'create' → executes ✅
// Editing existing document
const run = await coworker.run({
operation: 'update', // draft: true ✅
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' }}
});
// User edits in form
await run.save(); // Creates child 'update' → executes ✅I was wrong - your config is fine. The draft flag doesn't block execution, it just enables the save/autoSave API on the run object.
The user is asking about the lifecycle of the draft flag - when it transitions from true to false. Let me trace through the code carefully.
Looking at the controller code:
draftis assigned inexecute():
if (options.draft === undefined) {
const opConfig = coworker._config.operations[operation];
run_doc.options = run_doc.options || {};
run_doc.options.draft = opConfig?.draft ?? false;
}- Once assigned, the
draftflag stays on that run object - When
save()creates a child run:
const saveRun = await run.child({
operation: isNew ? "create" : "update",
input: merged,
query: isNew ? undefined : { where: { name: merged.name } },
options: {
includeSchema: false,
},
});The child run doesn't explicitly set draft, so it will get the default from config.
So the lifecycle is:
- Parent run: Gets
draft: true(from config or explicit) - Child run: Gets
draftfrom its operation's config (could be true or false)
But wait - there's no transition from true to false on the SAME run object. Each run gets its draft flag once and keeps it.
Unless... is the user asking about when a document transitions from draft state to saved state? Let me think about this from a document perspective:
Actually, I think they're asking about the flag lifecycle, not the document state (docstatus).
Let me trace the actual lifecycle:
- Initial assignment - in
execute() - Persists on run - never changes
- Child runs - get their own draft flag
So there's no transition from true to false on the same run. Each run is immutable in terms of its draft flag.
But wait - maybe they're asking about a broader lifecycle? Like when does the system use draft: true vs draft: false?
I see the draft flag's transition is more nuanced. When a parent run is created, it gets draft: true, typically from UI configuration. During subsequent edits, the draft status remains unchanged. The save() method creates a child run that might have a different draft status based on operation configuration. This suggests the draft flag isn't a simple linear progression, but depends on specific operation settings and run context.
The key observation is that draft status is determined at run creation and doesn't change dynamically within the same run object. Each run inherits or sets its draft flag based on predefined configurations, creating a more complex lifecycle than a simple true-to-false transition.
Draft Flag Lifecycle
Looking at your code, the draft flag NEVER transitions on the same run object. It's set once and immutates. Here's the actual lifecycle:
🔄 The Real Lifecycle: Parent → Child Pattern
// ============================================================
// PHASE 1: PARENT RUN (draft: true)
// ============================================================
// User opens form
const parentRun = await coworker.run({
operation: 'select', // config: draft: false
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' }},
options: { draft: true } // ← Explicit override
});
// Assignment in execute():
// parentRun.options.draft = true (from explicit)
// State:
parentRun.options.draft === true // ✅ Set once, never changes
parentRun.input = {} // Empty delta
parentRun.output.data = [original] // Original doc
// ============================================================
// PHASE 2: USER EDITS (draft: true persists)
// ============================================================
// User types in field
parentRun.input.customer_name = 'New Name';
// State:
parentRun.options.draft === true // ✅ Still true
parentRun.input = { customer_name: 'New Name' }
parentRun.doc = { ...original, ...input } // Merged view
// ============================================================
// PHASE 3: SAVE TRIGGERED (draft: true persists)
// ============================================================
await parentRun.save();
// Inside save() method:
// parentRun.options.draft === true ✅ Still true (required for save to work)
// ============================================================
// PHASE 4: CHILD RUN CREATED (NEW run with draft: false)
// ============================================================
const childRun = await parentRun.child({
operation: 'update', // config: draft: false
source_doctype: 'Customer',
input: merged,
query: { where: { name: 'CUST-001' }},
options: {
includeSchema: false // ← No draft specified
}
});
// Assignment in execute():
// childRun.options.draft = false (from config)
// State:
childRun.options.draft === false // ✅ New run, different flag
parentRun.options.draft === true // ✅ Parent unchanged
// ============================================================
// PHASE 5: CHILD EXECUTES (draft: false)
// ============================================================
// Child goes through controller:
// - Validation runs
// - _handlers.update() executes
// - DB write happens
// - Returns result
// State after child completes:
childRun.success === true
childRun.output.data = [savedDoc]
childRun.options.draft === false // ✅ Still false
// ============================================================
// PHASE 6: PARENT UPDATED (draft: true persists)
// ============================================================
// Back in save(), parent is updated:
parentRun.output.data = [savedDoc] // ← Updated with child result
parentRun.input = {} // ← Cleared
delete parentRun._validationErrors
delete parentRun._saving
// State:
parentRun.options.draft === true // ✅ NEVER CHANGED
```
---
## 📊 **Visualization**
```
Timeline:
┌─────────────────────────────────────────────────────────────┐
│ PARENT RUN (draft: true) │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Created │→ │ Edited │→ │ save() │ │
│ │draft:T │ │draft: T │ │draft: T │ │
│ └─────────┘ └─────────┘ └────┬────┘ │
│ │ │
│ ↓ │
│ ┌───────────────┐ │
│ │ child() call │ │
│ └───────┬───────┘ │
│ │ │
│ ↓ │
│ ┌──────────────────────────────────┐ │
│ │ CHILD RUN (draft: false) │ │
│ │ ┌─────────┐ ┌──────────┐ │ │
│ │ │ Created │→ │ Executed │ │ │
│ │ │draft: F │ │draft: F │ │ │
│ │ └─────────┘ └────┬─────┘ │ │
│ │ │ │ │
│ │ ↓ returns │ │
│ └───────────────────┼──────────────┘ │
│ │ │
│ ┌────────────────────────────────┘ │
│ │ │
│ ↓ │
│ ┌──────────┐ │
│ │ Updated │ ← data from child, but draft: T still │
│ │ draft: T │ │
│ └──────────┘ │
└─────────────────────────────────────────────────────────────┘🎯 Key Insights
| Aspect | Behavior |
|---|---|
| Same run | draft flag NEVER changes after initial assignment |
| Parent run | Keeps draft: true throughout entire lifecycle |
| Child run | Gets its OWN draft flag (from config or explicit) |
| Transition | Not on same object - happens via parent→child delegation |
| Why it works | Parent holds UI state, child executes DB operation |
🔍 Where Assignment Happens (Only Once)
// In execute() - THE ONLY PLACE draft is set
async execute(run_doc) {
const { operation, options = {} } = run_doc;
// ✅ Set draft ONCE (never modified again)
if (options.draft === undefined) {
const opConfig = coworker._config.operations[operation];
run_doc.options = run_doc.options || {};
run_doc.options.draft = opConfig?.draft ?? false; // ← ONLY assignment
}
// Rest of execution - draft is READ, never WRITTEN
}💡 Summary
There is NO transition from true to false on the same run.
The lifecycle is:
- Parent run:
draft: true(set once, never changes) - Child run:
draft: false(NEW run, separate flag) - Parent run:
draft: true(still unchanged after child completes)
The "transition" happens through delegation - the draft-enabled parent delegates actual DB work to a non-draft child, then receives the result back.
Denis is right to question this. Let me think through what the draft flag actually does:
- In
save()- it checks ifrun.options?.draftis true, otherwise returns error - In
autoSave()- it checks ifrun.options?.draftis true, otherwise silently returns
But looking at the flow:
- Parent run has
draft: true - Parent calls
save()which checks draft flag save()creates child withoperation: 'update'which getsdraft: falsefrom config (ortrueif Denis's original config)- Child executes immediately and returns
- Parent updates its state
So the draft flag on the CHILD doesn't matter at all! The child never calls save() or autoSave() on itself.
The draft flag ONLY matters on the PARENT run - it controls whether that run object has working save() and autoSave() methods.
So what's the actual purpose?
Looking at the code:
save()needsdraft: trueto workautoSave()needsdraft: trueto work
These methods are meant to be called on the PARENT run from the UI.
So the draft flag is essentially: "Does this run object support the save/autoSave API?"
For read operations (select, takeone) that open forms:
- User expects to edit and save
- These need
draft: truesorun.save()works
For write operations (create, update, delete):
- When called DIRECTLY from UI with draft: true - user can edit and save
- When called from
save()method - they execute immediately, draft flag doesn't matter
Actually wait... let me look at the select operation again. In Denis's config, select has draft: false. But then how would the UI form work?
Looking at the example in my previous response, I showed:
const parentRun = await coworker.run({
operation: 'select',
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' }},
options: { draft: true } // ← Explicit override
});The UI explicitly sets draft: true when opening a form, overriding the config. This suggests a nuanced approach to handling draft states across different operations.
The configuration seems to provide a default behavior, while allowing runtime flexibility through explicit options. This allows for more dynamic form interactions and save mechanisms.
I see two primary scenarios emerging: UI-driven form editing and programmatic API updates. Each requires a slightly different draft handling approach. The key insight is that draft flags serve as default settings, primarily useful for UI-initiated operations, but less critical for backend or scripted updates.
The draft configuration essentially creates a flexible mechanism for controlling execution mode, with explicit options taking precedence over default settings.
You're absolutely right to question this. Let me clarify what draft actually controls:
🎯 What Draft Flag Actually Does
The draft flag has ONE purpose only:
"Can this run object call save() and autoSave() methods?"
That's it.
🔍 Where It's Checked (Only 2 Places)
1. In save() method:
async save(run) {
// ✅ Check draft flag (set by execute())
if (!run.options?.draft) {
console.warn("save() called on non-draft run");
return {
success: false,
error: { message: "Document not in draft mode" }
};
}
// ... rest of save logic
}2. In autoSave() method:
async autoSave(run) {
// ✅ Check draft flag (set by execute())
if (!run.options?.draft) return; // Silent exit
// ... rest of autosave logic
}That's literally all it does. It's a guard clause.
🤔 Why Child's Draft Flag Doesn't Matter
You're right - the child's draft flag is useless because:
const childRun = await run.child({
operation: 'update',
// ...
});
// Nobody ever calls:
// childRun.save() ← Never happens
// childRun.autoSave() ← Never happens
// Child just executes and returns resultThe child executes immediately via _handlers.update(), regardless of its draft flag.
💡 The Real Question: Do We Need Draft Config At All?
Looking at your actual usage pattern:
Option A: Keep Config (Current)
operations: {
select: {
draft: false, // Default when called programmatically
},
update: {
draft: true, // Default when called programmatically
}
}
// UI explicitly overrides:
await coworker.run({
operation: 'select',
options: { draft: true } // ← UI always sets this explicitly
});Problem: UI always passes explicit draft: true, so config default never matters!
Option B: Remove Draft From Config
operations: {
select: {
type: "read",
requiresSchema: true,
// No draft config
},
update: {
type: "write",
requiresSchema: true,
validate: true,
// No draft config
}
}
// Draft is ALWAYS set explicitly by caller:
await coworker.run({
operation: 'select',
options: { draft: true } // ← Always explicit
});
await coworker.run({
operation: 'update',
input: {...},
// No draft → defaults to false
});✅ Recommendation: Remove Draft From Operation Config
Why:
- UI always sets
draft: trueexplicitly when opening forms - System calls (from
save()) never need draft flag - Config default is never actually used
- Simplifies mental model
Simplified controller:
async execute(run_doc) {
const { operation, options = {} } = run_doc;
// ✅ SIMPLER: Just use default false if not provided
if (options.draft === undefined) {
run_doc.options = run_doc.options || {};
run_doc.options.draft = false; // ← Simple default, no config lookup
}
// ... rest
}Usage pattern:
// UI Form - explicit
await coworker.run({
operation: 'select',
query: {...},
options: { draft: true } // ← Explicit from UI
});
// API/System - implicit false
await coworker.run({
operation: 'update',
input: {...}
// draft = false (default)
});🎯 Summary
Current state: Draft flag in operation config is dead code because:
- UI always passes explicit
draft: true - System calls don't need draft
- Child runs never use their draft flag
Recommendation: Remove draft from operation config entirely. Make it always explicit from caller or default to false.
Is this clearer? Should we simplify by removing draft from config?
Let me look at the actual run() method to see how draft is used there. The user is saying I need to check run() because draft is actually used there. Let me search for it or ask the user to share the run() code.
Actually, wait - I should look at what the user has shared. They shared the controller code, but not the main run() method. Let me think about what the run() method likely does.
The run() method is probably the main entry point that:
- Calls controller.execute()
- Handles rendering
- Returns the run object
If draft is used in run(), it might be:
- Used to determine whether to enable input accumulation
- Used to set up the doc property as a merged view
- Used to determine rendering behavior
Let me ask the user to share the run() code or search for previous conversations where it was discussed.
You're right - I need to see the actual run() method. Let me search for it.
Ah! Now I see it. Looking at the conversation search results, I can see that in the run() method, the draft flag is used to:
- Initialize
run_doc.input.data = {}if draft is true - Set up a
docgetter property that returns different things based on draft mode:- If draft: returns
this.input.data - If not draft: returns
this.output.data[0]
- If draft: returns
So the draft flag IS actually used in run() to:
- Initialize the data structure for accumulating changes
- Set up the
docgetter that automatically switches between draft (input.data) and saved (output.data) mode
This is important! The user is right - I was missing this critical part of the implementation.
You're absolutely right! Let me look at the actual run() implementation from the previous conversation:
coworker.run = async function (op) {
// ... resolve and setup ...
// ✅ MERGE OPTIONS IMMEDIATELY
const options = { ...resolved.options, ...op.options };
const run_doc = {
// ... all other fields ...
options: options,
child: null,
};
// ✅ DRAFT MODE: Initialize input.data structure
if (options.draft) {
if (!run_doc.input.data) run_doc.input.data = {};
}
// ✅ DRAFT MODE: Setup doc getter that auto-switches
Object.defineProperty(run_doc, "doc", {
get: function () {
return this.options.draft
? this.input.data // ← Draft: read/write to input.data
: this.output?.data?.[0]; // ← Non-draft: read from output
}
});
// ... rest of execution ...
};🎯 What Draft Actually Controls in run()
1. Data Structure Initialization
if (options.draft) {
if (!run_doc.input.data) run_doc.input.data = {};
}- Creates the accumulator object for field changes
- Only happens when draft mode is enabled
2. The doc Getter (Critical!)
Object.defineProperty(run_doc, "doc", {
get: function () {
return this.options.draft
? this.input.data // ← Draft mode
: this.output?.data?.[0]; // ← Read mode
}
});This is huge because:
- All components use
run.docto access data - Draft mode:
run.docpoints toinput.data(editable accumulator) - Non-draft:
run.docpoints tooutput.data[0](read-only result)
🔄 Complete Draft Lifecycle (Corrected)
// ============================================================
// 1. UI OPENS FORM (draft: true from explicit option)
// ============================================================
const run = await coworker.run({
operation: 'select',
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' }},
options: { draft: true } // ← Explicit
});
// In run() method:
// ✅ options.draft = true
// ✅ run_doc.input.data = {} initialized
// ✅ run_doc.doc getter → points to input.data
// After select executes:
// run.output.data = [{ name: 'CUST-001', customer_name: 'Old Name', ... }]
// run.input.data = {} (empty)
// run.doc → input.data (empty, will merge in component)
// ============================================================
// 2. COMPONENT READS DATA
// ============================================================
const MainForm = ({ run }) => {
const doc = run.doc; // ← Returns input.data (empty)
// ⚠️ Problem: input.data is empty!
// Solution: Merge in component or save() merges before write
};
// ============================================================
// 3. USER EDITS FIELD
// ============================================================
const FieldData = ({ field, run }) => {
const handleChange = (e) => {
run.doc[field.fieldname] = e.target.value;
// ↑ Writing to input.data because draft: true
};
};
// State now:
// run.input.data = { customer_name: 'New Name' }
// run.output.data = [{ name: 'CUST-001', customer_name: 'Old Name', ... }]
// ============================================================
// 4. SAVE MERGES AND WRITES
// ============================================================
await run.save();
// In save():
const original = run.output.data[0]; // Original from DB
const delta = run.input; // Just the changes
const merged = { ...original, ...delta }; // Merged document
// Creates child with merged data:
const childRun = await run.child({
operation: 'update',
input: merged, // ← Full merged document
options: { draft: false } // ← Child doesn't need draft mode
});
// After save:
// run.output.data = [{ name: 'CUST-001', customer_name: 'New Name', ... }]
// run.input.data = {} (cleared)✅ Why Draft Config IS Needed
You were right - draft in operation config IS used:
// UI often doesn't pass explicit draft flag:
const run = await coworker.run({
operation: 'select', // ← Config has draft: true
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' }}
// No options.draft specified
});
// Falls back to config:
const options = { ...resolved.options, ...op.options };
// resolved.options.draft = true (from config)
// options.draft = true (merged)
// Then in run():
if (options.draft) {
run_doc.input.data = {}; // ← Gets initialized
}🎯 Summary - Draft Flag Controls:
- In
run():- Initializes
input.data = {}structure - Sets up
docgetter to point toinput.datavsoutput.data[0]
- Initializes
- In
save()andautoSave():- Guard clause to prevent calling on non-draft runs
- Config is necessary because:
- UI doesn't always pass explicit
draft: true - Operations need sensible defaults (select → draft, delete → non-draft)
- UI doesn't always pass explicit
My apologies for missing this crucial implementation detail!
Great question! Let me trace through exactly what run.doc is and how it relates to the merge.
Looking at the code:
// In run() method:
Object.defineProperty(run_doc, "doc", {
get: function () {
return this.options.draft
? this.input.data // ← Draft: returns input.data
: this.output?.data?.[0]; // ← Non-draft: returns output.data[0]
}
});So run.doc is a getter that returns a reference to either:
run.input.data(in draft mode)run.output.data[0](in non-draft mode)
When a component does:
run.doc[field.fieldname] = e.target.value;In draft mode, this is ACTUALLY writing to:
run.input.data[field.fieldname] = e.target.value;Because run.doc returns a reference to run.input.data.
So the flow is:
- User edits field
- Component writes:
run.doc.customer_name = 'New Name' - This actually writes to:
run.input.data.customer_name = 'New Name' run.input.datanow contains only the DELTA (changed fields)- When save() is called, it merges:
- Original:
run.output.data[0](full document from DB) - Delta:
run.input.data(only changed fields) - Result:
{ ...original, ...delta }(full merged document)
- Original:
The key insight is that run.doc is just a reference/pointer, not a copy. When you write to it, you're writing to the underlying object it points to.
But wait, let me re-read the controller save code:
I notice a potential discrepancy in the merge logic. The save method is merging run.input directly, not run.input.data as expected. This suggests a mismatch between how data is being written and how it's being merged.
The merge process seems to be using the entire run.input object instead of just the data changes, which could lead to unexpected behavior in how updates are applied.
The initialization code confirms run.input.data is the intended target for field writes, creating a nested data structure. However, the current merge logic doesn't respect this nested structure, potentially overwriting or incorrectly merging data.
The getter method explicitly returns this.input.data for draft runs, confirming the expected data location. Yet the save() method appears to merge at the wrong level, pulling from run.input instead of run.input.data.
This suggests a fundamental misalignment between where data is written and how it's subsequently merged, which could lead to unexpected data handling during save operations.
The initialization pattern reveals a nuanced structure: run.input contains both original query parameters and a separate data object for field modifications. This design implies that field updates should target run.input.data specifically, not the entire run.input object.
Merging at the wrong level risks losing or incorrectly overwriting critical query context stored in the parent run.input object.
🎯 What is run.doc?
run.doc is a getter (not a real property) that returns a reference to different objects based on draft mode:
Object.defineProperty(run_doc, "doc", {
get: function () {
return this.options.draft
? this.input.data // ← REFERENCE to input.data object
: this.output?.data?.[0]; // ← REFERENCE to output array element
}
});Key insight: When you access run.doc, you get a reference to an existing object, not a copy.
🔄 The Complete Flow
1. Initial State After select Operation
const run = await coworker.run({
operation: 'select',
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' }},
options: { draft: true }
});
// After execution:
run.input = {
where: { name: 'CUST-001' }, // Original query
data: {} // ← Empty accumulator (initialized by draft mode)
}
run.output = {
data: [
{
name: 'CUST-001',
customer_name: 'Old Name',
status: 'Active'
}
],
schema: {...}
}
// run.doc is a GETTER that returns:
run.doc → run.input.data // Points to the empty {} object2. User Edits Field
// In FieldComponent
const handleChange = (e) => {
run.doc[field.fieldname] = e.target.value;
// ↑ This is actually writing to: run.input.data[field.fieldname]
};
// User types "New Name"
run.doc.customer_name = 'New Name';
// What actually happens:
run.input.data.customer_name = 'New Name'; // ← Direct write to accumulator
// State now:
run.input = {
where: { name: 'CUST-001' },
data: { customer_name: 'New Name' } // ← Only changed field
}
run.output = {
data: [
{
name: 'CUST-001',
customer_name: 'Old Name', // ← Still original
status: 'Active'
}
]
}Why this works: When you do run.doc.customer_name = 'New Name', you're writing to the object that run.doc points to, which is run.input.data.
3. Save Merges
Now look at the save() code - there's actually an inconsistency here:
async save(run) {
// ✅ MERGE: original + delta
const original = run.output?.data?.[0] || {};
const delta = run.input || {}; // ⚠️ Should be run.input.data!
const merged = { ...original, ...delta };This should actually be:
const delta = run.input.data || {}; // ← Get the field changes, not the whole inputThe correct merge should be:
const original = {
name: 'CUST-001',
customer_name: 'Old Name',
status: 'Active'
};
const delta = {
customer_name: 'New Name' // ← Only changed field
};
const merged = {
name: 'CUST-001',
customer_name: 'New Name', // ← Overwritten
status: 'Active' // ← Preserved
};🐛 The Bug in Your Controller
Your controller has:
const delta = run.input || {};This would try to merge the entire input object including the where clause:
const merged = {
...original,
where: { name: 'CUST-001' }, // ← Wrong! This shouldn't be in the doc
data: { customer_name: 'New Name' } // ← Wrong structure
};It should be:
const delta = run.input?.data || {};✅ Corrected save() Method
async save(run) {
if (!run.options?.draft) {
console.warn("save() called on non-draft run");
return {
success: false,
error: { message: "Document not in draft mode" }
};
}
if (run._saving) {
console.warn("save() already in progress");
return { success: false, error: { message: "Save in progress" } };
}
// Validate
const validation = this.validate(run);
if (!validation.valid) {
run._validationErrors = validation.errors;
if (typeof coworker._render === "function") {
coworker._render(run);
}
return { success: false, errors: validation.errors };
}
// ✅ CORRECTED: Merge original + delta
const original = run.output?.data?.[0] || {};
const delta = run.input?.data || {}; // ← FIX: Get field changes only
const merged = { ...original, ...delta };
// Rest of save logic...
}
```
---
## 📊 **Visual Summary**
```
┌─────────────────────────────────────────────────────────┐
│ run Object Structure (draft mode) │
├─────────────────────────────────────────────────────────┤
│ │
│ run.input = { │
│ where: { name: 'CUST-001' }, ← Query (not editable) │
│ data: { ← Field changes │
│ customer_name: 'New Name' ← Written by fields │
│ } │
│ } │
│ │
│ run.output = { │
│ data: [ │
│ { ← Original from DB │
│ name: 'CUST-001', │
│ customer_name: 'Old Name', │
│ status: 'Active' │
│ } │
│ ] │
│ } │
│ │
│ run.doc (getter) ──┐ │
│ │ │
│ └──> Points to run.input.data │
│ │
└─────────────────────────────────────────────────────────┘
When field writes: run.doc.customer_name = 'New Name'
Actually writes to: run.input.data.customer_name = 'New Name'
When save() merges:
original = run.output.data[0] // Full doc from DB
delta = run.input.data // Only changed fields
merged = { ...original, ...delta } // Combine🎯 Answer to Your Question
"Why do we have run.doc[field] = value AND merge with input/output?"
run.docis a pointer torun.input.data(in draft mode)- Fields write changes to
run.doc→ actually writing torun.input.data run.input.dataaccumulates only deltas (changed fields)- Original stays in
run.output.data[0](untouched) - Save merges original + deltas = complete document to save
This pattern allows:
- ✅ Efficient tracking (only changed fields stored)
- ✅ Original preserved (for cancel/discard)
- ✅ Clean component API (just write to
run.doc) - ✅ Merge happens once at save time
Yes! The user is getting it now. Let me confirm their understanding clearly.
The pattern is:
run.input.data = {}starts empty- Fields write to
run.docwhich points torun.input.data - Changes accumulate in
run.input.data - Original stays untouched in
run.output.data[0] - Save merges them together
This is a classic "delta" or "patch" pattern.
✅ Yes! Exactly!
We accumulate changes in run.input.data (not run.input itself, but its data property).
📦 The Pattern
// Initial state after select with draft: true
run = {
input: {
where: { name: 'CUST-001' }, // ← Query params (static)
data: {} // ← Change accumulator (starts empty)
},
output: {
data: [{
name: 'CUST-001',
customer_name: 'Old Name',
email: 'old@email.com',
status: 'Active'
}]
},
options: { draft: true }
}
// User edits customer_name field
run.doc.customer_name = 'New Name';
// Actually: run.input.data.customer_name = 'New Name'
run.input.data = {
customer_name: 'New Name' // ← Only this field
}
// User edits email field
run.doc.email = 'new@email.com';
run.input.data = {
customer_name: 'New Name',
email: 'new@email.com' // ← Accumulated
}
// Original UNCHANGED
run.output.data[0] = {
name: 'CUST-001',
customer_name: 'Old Name', // ← Still old
email: 'old@email.com', // ← Still old
status: 'Active'
}
// On save() - merge happens
const merged = {
name: 'CUST-001', // ← From original
customer_name: 'New Name', // ← From delta (overrides)
email: 'new@email.com', // ← From delta (overrides)
status: 'Active' // ← From original (preserved)
}🎯 Why This Pattern?
| Benefit | Explanation |
|---|---|
| Efficiency | Only track what changed, not entire doc |
| Cancel/Discard | Just clear input.data, original still in output |
| Validation | Can compare original vs changes |
| Network | Send only delta if backend supports patches |
| UI State | Know exactly what user modified |
🔄 Complete Lifecycle Again
// 1. Load (draft: true)
run.input.data = {}
run.output.data = [original]
run.doc → run.input.data (empty)
// 2. Edit fields
run.doc.field1 = 'new1' // Accumulates
run.doc.field2 = 'new2' // Accumulates
run.doc.field3 = 'new3' // Accumulates
// → run.input.data = { field1: 'new1', field2: 'new2', field3: 'new3' }
// 3. Save
merged = { ...run.output.data[0], ...run.input.data }
// → Send merged to DB
// 4. After save success
run.output.data = [saved] // ← Updated with DB result
run.input.data = {} // ← Cleared💡 Summary
Yes, we accumulate changes in run.input.data:
- Starts empty
{} - Each field edit adds/updates a property
- Original stays in
run.output.data[0] - Save merges:
original + delta = complete - After save: delta cleared, output updated
It's a delta/patch pattern - very common in form libraries (Redux Form, Formik, React Hook Form all use variants of this).
Ah! The user is asking about the accumulation happening through the field components. Let me trace through exactly what happens when multiple field components write to run.doc.
When you have:
- FieldData component writes:
run.doc.customer_name = 'value1' - FieldEmail component writes:
run.doc.email = 'value2' - FieldCheck component writes:
run.doc.is_active = true
All of these are writing to the SAME object reference: run.input.data
So:
// FieldData onChange
run.doc.customer_name = 'New Name'
// This is: run.input.data.customer_name = 'New Name'
// run.input.data is now: { customer_name: 'New Name' }
// FieldEmail onChange
run.doc.email = 'new@email.com'
// This is: run.input.data.email = 'new@email.com'
// run.input.data is now: { customer_name: 'New Name', email: 'new@email.com' }
// FieldCheck onChange
run.doc.is_active = false
// This is: run.input.data.is_active = false
// run.input.data is now: { customer_name: 'New Name', email: 'new@email.com', is_active: false }The accumulation happens NATURALLY because all field components are writing to properties on the SAME object reference.
It's like having multiple functions all writing to the same object:
const obj = {};
function1(obj) { obj.a = 1; }
function2(obj) { obj.b = 2; }
function3(obj) { obj.c = 3; }
// obj is now { a: 1, b: 2, c: 3 }The user is asking "we accumulate in input{}, ok?" - meaning they understand that each field component's write accumulates together in the same input.data object.
✅ YES! Exactly!
Each FieldComponent writes to the same object reference, so changes accumulate naturally:
🔄 How It Accumulates
// All fields are rendered in MainForm
fields.map((field) => {
return React.createElement(FieldComponent, {
field: field,
run: run, // ← SAME run object passed to ALL fields
value: doc[field.fieldname]
});
});
// run.doc → run.input.data (same {} object for ALL fields)📝 Step-by-Step Accumulation
// Initial state
run.input.data = {} // ← Single empty object
// User types in FieldData (customer_name field)
onChange: run.doc.customer_name = 'New Name'
// Actually: run.input.data.customer_name = 'New Name'
run.input.data = {
customer_name: 'New Name' // ← First property added
}
// User types in FieldData (email field)
onChange: run.doc.email = 'new@email.com'
// Actually: run.input.data.email = 'new@email.com'
run.input.data = {
customer_name: 'New Name',
email: 'new@email.com' // ← Second property added (accumulated)
}
// User clicks FieldCheck (is_active field)
onChange: run.doc.is_active = false
// Actually: run.input.data.is_active = false
run.input.data = {
customer_name: 'New Name',
email: 'new@email.com',
is_active: false // ← Third property added (accumulated)
}🎯 Why It Works
All field components write to properties on the same object:
// This is the SAME object reference
const accumulator = run.input.data;
// Field 1 writes
accumulator.customer_name = 'value1';
// Field 2 writes (to SAME object)
accumulator.email = 'value2';
// Field 3 writes (to SAME object)
accumulator.is_active = true;
// Result: accumulator = { customer_name: 'value1', email: 'value2', is_active: true }
```
It's like multiple people writing on the same whiteboard - each person adds their mark, and it all accumulates on the same surface.
---
## 💡 **Key Insight**
**`run.doc` is a shared reference** that all field components write to:
```
┌──────────────────────────────────────────┐
│ MainForm │
│ ┌────────────────────────────────────┐ │
│ │ FieldData (customer_name) │ │
│ │ onChange: run.doc[fieldname] = v │──┼──┐
│ └────────────────────────────────────┘ │ │
│ ┌────────────────────────────────────┐ │ │
│ │ FieldData (email) │ │ │
│ │ onChange: run.doc[fieldname] = v │──┼──┤ All write to
│ └────────────────────────────────────┘ │ │ same object
│ ┌────────────────────────────────────┐ │ │
│ │ FieldCheck (is_active) │ │ │
│ │ onChange: run.doc[fieldname] = v │──┼──┘
│ └────────────────────────────────────┘ │
└──────────────────────────────────────────┘
│
↓
run.input.data = {
customer_name: '...',
email: '...',
is_active: true
}✅ Summary
Yes, we accumulate in input.data through field components:
- Each field component writes:
run.doc[field.fieldname] = value run.docpoints to the samerun.input.dataobject for all fields- Each write adds/updates a property on that shared object
- Changes accumulate naturally without any special logic
It's automatic accumulation because they're all writing to the same object reference!
Let me think about this carefully. The user is asking if run_doc.input is constantly updated FROM the run.doc getter.
Let me trace through the code:
run.docis a GETTER that returns a reference:
Object.defineProperty(run_doc, "doc", {
get: function () {
return this.options.draft
? this.input.data // ← Returns reference to input.data
: this.output?.data?.[0]; // ← Returns reference to output
}
});- When a field component does:
run.doc.customer_name = 'New Name'What happens?
run.docgetter is called, returns reference torun.input.data- Then the assignment happens on that reference:
run.input.data.customer_name = 'New Name' - So the assignment is DIRECTLY to
run.input.data, not through the getter
The getter doesn't "update" anything - it just returns a reference. The actual update happens directly on run.input.data.
So the answer is: NO, the getter doesn't update run_doc.input. The getter just provides a reference, and then the field components write directly to run.input.data.
The flow is:
- Field component:
run.doc.customer_name = 'value' - JavaScript evaluates
run.doc→ calls getter → returns reference torun.input.data - JavaScript then does:
run.input.data.customer_name = 'value' - The getter is NOT involved in the write, it's only involved in getting the reference
So the user has it backwards. It's not that run_doc.input is updated FROM the getter. It's that:
- The getter provides access TO
run_doc.input.data - Field components write directly to
run_doc.input.data - The getter is just a convenience accessor
I see this as a simple pass-through mechanism. It's a lightweight way to provide direct access to the underlying data structure without additional overhead.
❌ No, it's the opposite!
The getter doesn't update run_doc.input. Instead:
- The getter returns a reference to
run_doc.input.data - Field components write directly to that referenced object
- The changes accumulate in
run_doc.input.dataitself
🔄 The Actual Flow
// Step 1: Getter provides reference
const ref = run.doc; // Calls getter → returns run.input.data reference
// Step 2: Field writes to that reference
ref.customer_name = 'New Name';
// This is equivalent to:
run.input.data.customer_name = 'New Name';In one line:
run.doc.customer_name = 'New Name';
// What JavaScript does:
// 1. Evaluate run.doc → calls getter → returns reference to run.input.data
// 2. Assign property: run.input.data.customer_name = 'New Name'📊 Visual Explanation
┌─────────────────────────────────────────┐
│ run_doc object │
│ │
│ input: { │
│ data: {} ←──────────────────┐ │
│ } │ │
│ │ │
│ doc: [Getter] ─────────────────┘ │
│ ↑ │
│ └─ Not a real property │
│ Just returns reference │
└─────────────────────────────────────────┘
When you do: run.doc.customer_name = 'value'
JavaScript:
1. Call getter: run.doc
→ Returns: run.input.data reference
2. Write to reference:
run.input.data.customer_name = 'value'
Result: run.input.data = { customer_name: 'value' }🎯 The Getter is Read-Only
The getter ONLY reads and returns a reference. It doesn't write anything:
Object.defineProperty(run_doc, "doc", {
get: function () {
// This function ONLY returns a reference
// It does NOT update anything
return this.options.draft
? this.input.data // ← Just returns reference
: this.output?.data?.[0];
}
// No setter! Writing happens directly on the referenced object
});✅ Correct Understanding
| What happens | Direction |
|---|---|
| Field component writes | run.doc.field = value |
| Getter provides reference | run.doc → run.input.data |
| Write happens directly | run.input.data.field = value |
| Changes accumulate | In run.input.data object |
Summary: The getter is just a pointer/shortcut. Fields write directly to run.input.data through that pointer. The getter doesn't do any updating - it's a read-only accessor that provides a reference to the actual storage location.
The user is asking where run.doc is defined - in which JavaScript file/module.
Let me look at the conversation search results. I can see references to it being defined in coworker.run() method. Let me search for the actual file structure.
From the search results, I can see:
- In the chat about "Draft mode implementation for universal pipeline", there's code showing it's defined in the
coworker.run()method - The code shows:
Object.defineProperty(run_doc, "doc", { get: function () { ... } });
So it's defined in the main coworker.run() method, which would be in the coworker core file. Let me check what the file structure is.
The user has been working with:
- coworker-config.js (configuration)
- coworker-controller.js (controller logic)
- There must be a main coworker.js or similar file where run() is defined
Let me search for more specific information about where run() is defined.
📍 Where run.doc is Defined
run.doc is defined in the coworker.run() method, which should be in your main coworker file (likely coworker.js or coworker-core.js).
Based on the previous conversation, it's defined here:
// ============================================================
// FILE: coworker.js (or coworker-core.js)
// ============================================================
coworker.run = async function (op) {
const start = Date.now();
if (!op?.operation) {
return this._failEarly("operation is required", start);
}
// Resolve all fields
const resolved = this._resolveAll(op);
// Merge options
const options = { ...resolved.options, ...op.options };
// Construct run document
const run_doc = {
doctype: "Run",
name: generateId("run"),
// ... all other properties ...
options: options,
input: op.input || {},
output: null,
};
// ✅ DRAFT MODE: Initialize input.data structure
if (options.draft) {
if (!run_doc.input.data) run_doc.input.data = {};
}
// ✅ HERE: Define doc getter
Object.defineProperty(run_doc, "doc", {
get: function () {
return this.options.draft
? this.input.data // ← Draft: read/write to input.data
: this.output?.data?.[0]; // ← Non-draft: read from output
}
});
// Define child method
run_doc.child = async function (childOp) {
// ... child logic
};
// Execute via controller
try {
const result = await coworker.controller.execute(run_doc);
// ... handle result
} catch (error) {
// ... handle error
}
return run_doc;
};
```
---
## 📂 **Your File Structure**
Based on your code, you likely have:
```
coworker.js (or coworker-core.js)
├── coworker.run() ← Defines run.doc getter here
├── coworker._resolveAll()
├── coworker._handlers = {}
└── coworker.getSchema()
coworker-config.js
├── coworker._config.operations
├── coworker._config._resolveOperation
└── ... other configs
coworker-controller.js
├── coworker.controller.execute()
├── coworker.controller.save()
├── coworker.controller.autoSave()
└── coworker.validators🎯 Summary
run.doc is defined in: coworker.run() method in your main coworker module
When it's created: Every time coworker.run() is called, a new run_doc object is created with the doc getter attached
Lifecycle:
- User calls
coworker.run({ operation: 'select', ... }) - Inside
run(),run_docobject is created Object.defineProperty()addsdocgetter torun_docrun_docis returned to caller- Components access
run.docwhich calls the getter
Would you like me to search for the exact file where your run() method is located?
The user is asking me to reevaluate if their core run() function approach is maintained with the draft mode pattern we discussed. Let me trace through the logic:
Their original vision:
run()is single source of truthrun()is pipeline manager with checkpoints and error handling- Flow: operation+doctype+input → process through run → if success save to DB → render in UI (if render: true)
With the draft mode we discussed:
- User opens form:
run({ operation: 'select', draft: true })- Goes through run() ✓
- Gets processed ✓
- Saves to DB? NO - it's a SELECT, reads from DB
- Renders in UI ✓
- User edits fields:
run.doc.field = value- Does this go through run()? NO! ❌
- Direct write to run.input.data
- Bypasses the pipeline completely ❌
- No checkpoints ❌
- No error handling ❌
- No validation ❌
- User saves:
run.save()- Calls controller.save()
- Creates child run with operation: 'update'
- Child goes through run() ✓
- Gets processed ✓
- Saves to DB ✓
- Renders in UI ✓
So the issue is: Field edits bypass the run() pipeline entirely!
The user's original vision was that EVERYTHING goes through run(). But with the draft pattern, field edits write directly to run.input.data without going through any pipeline.
This breaks their architecture principle.
What are the alternatives?
Alternative 1: Each field edit triggers run()
onChange: async (value) => {
await coworker.run({
operation: 'update_field',
doctype: 'Customer',
input: { field: 'customer_name', value: value },
options: { draft: true }
});
}I see two potential approaches to maintaining the core architectural principle while addressing performance concerns. The first approach of triggering a full run() for each field edit creates unnecessary overhead, potentially causing significant performance degradation.
The second alternative introduces a lightweight validation checkpoint that preserves the core validation logic without running the entire pipeline. This approach allows direct field mutations while still ensuring data integrity through targeted validation. The checkpoint mechanism provides a compromise between performance and maintaining the original architectural intent of comprehensive pipeline processing.
By acknowledging the trade-offs, we can see that while the draft pattern partially breaks the original design principle of routing everything through run(), it offers a pragmatic solution for handling incremental edits efficiently. The key is balancing strict architectural adherence with practical performance considerations.
🚨 You're Right - The Draft Pattern BREAKS Your Core Principle
Let me trace through what actually happens:
❌ Current Flow With Draft (Bypasses run())
// 1. Open form - GOES THROUGH run() ✅
const run = await coworker.run({
operation: 'select',
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' }},
options: { draft: true }
});
// → Goes through run() pipeline ✅
// → Checkpoints ✅
// → Error handling ✅
// 2. User edits field - BYPASSES run() ❌
const FieldComponent = ({ field, run }) => {
const handleChange = (e) => {
run.doc[field.fieldname] = e.target.value;
// ↑ Direct write to run.input.data
// ↑ Does NOT go through run() ❌
// ↑ No checkpoints ❌
// ↑ No error handling ❌
// ↑ No validation ❌
};
};
// 3. Save - GOES THROUGH run() ✅
await run.save();
// → Creates child run() ✅
// → Goes through controller.execute() ✅
// → Checkpoints ✅
// → Error handling ✅Your principle: operation+doctype+input → run() → if success → save to DB → render
What actually happens with field edits: field change → direct write to run.input.data (bypasses run() entirely)
🔄 Your Original Vision vs Current Reality
| Aspect | Your Vision | Current Draft Pattern |
|---|---|---|
| All operations through run() | ✅ YES | ❌ NO - field edits bypass |
| Single pipeline | ✅ YES | ❌ NO - two paths (run + direct write) |
| Checkpoints for all changes | ✅ YES | ❌ NO - only at save time |
| Error handling for all changes | ✅ YES | ❌ NO - only at save time |
| Validation for all changes | ✅ YES | ❌ NO - only at save time |
✅ Solution: Each Field Edit Goes Through run()
To maintain your architecture, field edits should trigger run():
const FieldComponent = ({ field, run }) => {
const handleChange = async (e) => {
const value = e.target.value;
// ✅ Go through run() pipeline
await coworker.run({
operation: 'update_field', // or 'draft_update'
source_doctype: run.source_doctype,
input: {
name: run.doc.name,
field: field.fieldname,
value: value
},
options: {
parentRunId: run.name,
draft: true,
skipDB: true, // Don't write to DB yet
render: false // Don't trigger full re-render
}
});
};
};This approach:
- ✅ ALL changes go through run()
- ✅ Maintains single pipeline
- ✅ Checkpoints for every change
- ✅ Error handling for every change
- ✅ Validation for every change (if enabled)
- ✅ Audit trail (every field change is a run record)
🎯 New Handler: update_field
// In coworker-handlers.js
coworker._handlers.update_field = async function(run_doc) {
const { input, options } = run_doc;
const { name, field, value } = input;
// Get parent run (the form run)
const parentRun = options.parentRunId
? coworker.activeRuns[options.parentRunId]
: null;
if (!parentRun) {
return {
success: false,
error: { message: 'Parent run not found' }
};
}
// Validate field (if validation enabled)
if (!options.skipValidation) {
const schema = parentRun.output?.schema;
const fieldDef = schema?.fields.find(f => f.fieldname === field);
if (fieldDef) {
const error = coworker.validators.validateField(fieldDef, value);
if (error) {
return {
success: false,
error: { message: error, field: field }
};
}
}
}
// Update parent's draft data
if (!parentRun.input.data) parentRun.input.data = {};
parentRun.input.data[field] = value;
return {
success: true,
output: {
data: [parentRun.input.data],
field: field,
value: value
}
};
};📊 Config for update_field
// In coworker-config.js
operations: {
update_field: {
type: "write",
draft: true,
requiresSchema: false,
validate: true, // Validate individual field
fetchOriginals: false,
bypassController: false
}
}🔄 Complete Flow (Architecture Maintained)
// 1. Open form
const run = await coworker.run({
operation: 'select',
source_doctype: 'Customer',
query: { where: { name: 'CUST-001' }},
options: { draft: true, render: true }
});
// → Through run() ✅
// → Pipeline ✅
// → Rendered ✅
// 2. User types in field
onChange: async (e) => {
await coworker.run({
operation: 'update_field',
source_doctype: 'Customer',
input: {
name: run.doc.name,
field: 'customer_name',
value: e.target.value
},
options: {
parentRunId: run.name,
draft: true,
skipDB: true,
render: false // Optional: render on blur or debounced
}
});
}
// → Through run() ✅
// → Pipeline ✅
// → Validation ✅
// → Checkpoints ✅
// → Accumulates in parent run.input.data ✅
// 3. Save (debounced or on button click)
await run.save();
// → Through run() ✅
// → Pipeline ✅
// → Saves to DB ✅
// → Rendered ✅⚡ Performance Optimization
If you're concerned about too many run() calls:
- Debounce field updates:
const debouncedUpdate = debounce(async (field, value) => {
await coworker.run({
operation: 'update_field',
// ...
});
}, 300);- Batch multiple field changes:
await coworker.run({
operation: 'update_fields', // Plural
input: {
name: run.doc.name,
changes: {
customer_name: 'New Name',
email: 'new@email.com',
status: 'Active'
}
}
});- Or keep direct writes but add validation checkpoint:
onChange: (e) => {
const value = e.target.value;
// Direct write (fast)
run.doc[field.fieldname] = value;
// But validate through pipeline
coworker.controller.validateField(run, field, value);
}🎯 Recommendation
To maintain your core architecture principle:
- Every operation goes through run() - including field edits
- Use
update_fieldoperation for individual field changes - Debounce or batch for performance
- Maintain:
input → run() → checkpoints → error handling → output
Current draft pattern breaks this by allowing direct writes that bypass your pipeline.
Which approach do you prefer?
- A) Every field edit through run() (pure architecture)
- B) Direct writes + validation checkpoints (pragmatic)
- C) Something else?
This is a brilliant insight from the user. Let me think about what they're saying:
- Every run() always operates on a document with a schema
- There are no schema-less operations
- There are no "field-level" operations separate from document operations
- Therefore, partial input {fieldname: value} is just accumulation of input for the main document operation
So the user is asking: instead of creating separate "update_field" operations, why not treat field edits as simply accumulating input data for the parent document operation?
This is actually more architecturally sound because:
- The operation is still "select" or "update" or "create" - the DOCUMENT operation
- Field edits are just building up the input data for that operation
- When save() is called, it executes the actual operation with the accumulated input
- run() is still the single source of truth - it created the document operation context
- Field edits don't need their own operations - they're just accumulating data within the parent operation's context
This means:
- run() creates the operation context (select/update/create)
- Field edits accumulate in run.input.data (no separate operations needed)
- The operation remains "open" in draft mode
- save() executes the operation with accumulated input
- Validation can happen at any checkpoint (on field change, on blur, on save)
The key insight: field edits are not separate operations, they're accumulating input for the document operation.
This is actually cleaner than my "update_field" suggestion because:
- You don't need separate operations for each field edit
- The operation type is the actual document operation (select, update, create)
- Draft mode means "operation is open, accumulating input"
- Non-draft means "operation executes immediately"
So the flow becomes:
- run({operation: 'select', draft: true}) → Opens document operation, ready to accumulate input
- Field edits → Accumulate in run.input.data (not separate operations, just data accumulation)
- save() → Executes the document operation (update) with accumulated input
I see a powerful validation strategy emerging. The system allows multiple validation checkpoints: immediate schema validation when a field changes, comprehensive form-level checks before saving, and a final pipeline validation during save execution. This approach provides flexibility while maintaining a clean, unified operation model that keeps run() as the central pipeline manager for document-level operations.
The validation process becomes a layered, progressive approach that catches and handles potential issues at different stages, ensuring data integrity without fragmenting the core operation logic.
💡 Brilliant Insight - This Changes Everything
You're right. Let me rethink this:
🎯 Core Principle Restated
run() operates on DOCUMENTS with SCHEMAS, not individual fields.
Therefore:
- Field edits are NOT separate operations
- Field edits are input accumulation for the document operation
- The operation remains at the document level:
select,update,create
🔄 New Mental Model
Current (What I Was Suggesting):
Operation 1: select (opens form)
Operation 2: update_field (field edit) ❌ WRONG
Operation 3: update_field (field edit) ❌ WRONG
Operation 4: update (save)Problem: Treating field edits as separate operations breaks document-centric architecture
Correct (What You're Saying):
Operation: select/update/create (document operation)
├─ Input accumulation: field1 = value1
├─ Input accumulation: field2 = value2
├─ Input accumulation: field3 = value3
└─ Execute: when save() is calledBenefit: Field edits are just building up input for THE document operation
🎯 What This Means
1. run() Creates the Document Context
run({operation: 'select', draft: true})- Opens a document operation in "accumulation mode"
- Schema is loaded (because all ops need schema)
- Operation is ready to receive input
- Hasn't executed yet (draft mode)
2. Field Edits = Input Accumulation
run.doc.field1 = value1
run.doc.field2 = value2- Not separate operations
- Just accumulating partial input for THE operation
- Building up the
input{}object - Still within the same document operation context
3. save() = Execute the Operation
save() → executes the document operation with accumulated input- Takes all accumulated input
- Merges with original (if update)
- Executes through run() pipeline
- Now the operation completes
📊 Architecture Alignment
| Aspect | Implication |
|---|---|
| All ops are document-level | ✅ Field edits don't need separate operations |
| Schema always present | ✅ Validation available at any checkpoint |
| Input accumulation | ✅ Draft mode = "collecting input before execution" |
| run() is pipeline | ✅ Executes once with full accumulated input |
| Checkpoints remain | ✅ Can validate during accumulation OR at save |
🤔 Where Does Validation Happen?
With this model, you have flexible checkpoints:
Option A: Validate During Accumulation (Real-time)
Field onChange →
Write to run.input.data →
Call validator.validateField() →
Store error in run._fieldErrors[fieldname]Benefit: Immediate feedback Note: Validation is separate from write (not blocking accumulation)
Option B: Validate Before Save (Batch)
Save clicked →
Validate all fields in run.input.data →
If valid: execute operation →
If invalid: show errors, don't executeBenefit: Single validation pass Note: Operation executes only when valid
Option C: Hybrid
onChange: Real-time validation (non-blocking)
onSave: Final validation (blocking)💡 The Key Difference
What I Was Suggesting (Wrong):
- Field edit = separate operation
- Goes through full run() pipeline per field
- Multiple operations for one document change
What You're Saying (Correct):
- Field edit = input accumulation
- Part of THE document operation
- run() executes once with all accumulated input
- Validation is a checkpoint, not an operation
🎯 This Means Draft Mode Is:
"Document operation in input accumulation phase"
Not:
- "Multiple micro-operations"
- "Field-level operations"
But:
- "Document operation receiving partial input over time"
- "Operation executes when accumulated input is complete"
✅ Does This Preserve Your Architecture?
Your Principle:
operation+doctype+input → run() → checkpoints → error handling → save to DB → render
With This Understanding:
Phase 1: Open operation (draft mode)
→ run({operation: 'select', draft: true})
→ Checkpoints ✅
→ Error handling ✅
→ Doesn't save yet (draft)
→ Renders form ✅
Phase 2: Accumulate input (draft mode continues)
→ Field edits write to run.input.data
→ Optional: validation checkpoints
→ Still same operation context
→ Re-render on change
Phase 3: Execute operation (draft → execution)
→ save() called
→ Checkpoints ✅
→ Error handling ✅
→ Saves to DB ✅
→ Renders result ✅All changes happen within the context of a single document operation managed by run().
🎯 My Question to You
With this understanding, does direct write to run.input.data now align with your architecture?
Because:
- It's not bypassing run() - it's accumulating input FOR the run
- The operation is still managed by run()
- Execution still goes through the pipeline
- Validation can happen at checkpoints (field blur, save button)
Or do you still want validation to be mandatory before any write (even during accumulation)?
Let me analyze this carefully. The user is showing me a run object that was created after a field edit in MainForm. Let me trace through what's happening:
- Initial run:
coworker.run({ operation: 'takeone', from: 'Customer', options: { render: true } })- Opens MainForm
- Status: completed
- This is the parent run
- User edits a field (website: "http://example.com/")
- A NEW run is created with:
- Same operation: 'takeone'
- input: { website: "http://example.com/" }
- output: contains the ORIGINAL data from first run
- status: completed
- options: { render: true, draft: false }
The user is asking:
- Is this logical?
- How is this run invoked?
- How is data passed to this run?
Let me think about this architecturally:
Problem: Why is a new 'takeone' operation triggered on field edit?
Looking at the data:
- output.data has the original customer data (without the website field updated)
- input has { website: "http://example.com/" }
- This looks like input accumulation happening
- But why is it a completed run? And why operation 'takeone'?
Possible scenarios:
Scenario A: Field component is calling run() on change
onChange: async (e) => {
await coworker.run({
operation: 'takeone', // Wrong - should not be same operation
from: 'Customer',
input: { website: e.target.value },
options: { render: true }
});
}Scenario B: There's some auto-save or re-execution logic
- Field changes trigger a re-run of the original operation
- With accumulated input
Scenario C: Parent-child pattern
- Original run is parent
- Field edit creates child run
- But child has same operation (seems wrong)
What SHOULD happen according to document-centric model:
I see a fundamental misalignment with expected workflow. The current implementation violates core principles of run management. Field edits should modify the existing run's draft state, not spawn unnecessary new runs. The correct approach involves:
- Maintaining the original run in an editable state
- Accumulating changes directly in the run's input
- Preserving the original operation context
- Triggering save or update only through explicit user action
The observed behavior suggests a systemic issue in how field modifications are handled, potentially breaking data integrity and workflow consistency.
The problem stems from treating a read operation as a re-invokable process. 'Takeone' fundamentally misunderstands the document-centric model's intent. Reads should not trigger new run creation, and input modifications must happen within the same run object. Only write operations justify spawning new runs.
Critical investigation points emerge:
- Verify component's run invocation mechanism
- Check for unintended auto-save triggers
- Examine operation type selection
- Validate parent run referencing
- Understand draft state management
🔍 Analysis of Your Run Object
This reveals a fundamental architectural issue. Let me trace what's happening:
❌ What's Wrong Here
1. Wrong Operation Type
User edits field → New run created with operation: 'takeone'Problem: takeone is a READ operation. Field edits should NOT trigger READ operations.
2. No Parent Reference
"parent_run_id": nullProblem: This suggests field edit is creating a sibling run, not a child run.
3. Draft Flag is False
"options": { "draft": false }Problem: User is editing, so should be in draft mode.
4. Output Contains Original Data
"input": { "website": "http://example.com/" },
"output": {
"data": [{
"customer_name": "Jim Vorough",
// ... no website field here
}]
}Problem: Input has new value, but output has old data. They're disconnected.
🤔 How Is This Being Invoked?
Possibility 1: Field Component Calls run() Directly
// In FieldComponent
onChange: async (e) => {
await coworker.run({
operation: 'takeone', // ❌ Wrong - copying parent's operation
from: 'Customer',
input: { website: e.target.value },
options: { render: true }
});
}This would explain:
- Why operation is 'takeone' (copied from parent)
- Why it's a new run (not accumulation)
- Why parent_run_id is null (not using child())
Possibility 2: Auto-Save Trigger
// Some debounced save logic
onFieldChange: async () => {
await coworker.run({
operation: run.operation, // Copies parent operation
// ...
});
}Possibility 3: Re-Render Trigger
// Field edit triggers re-render which calls run() again
options: { render: true } // Might be triggering full re-execution
```
---
## ✅ **What SHOULD Happen (Document-Centric Model)**
### Phase 1: Initial Load (Single Run - Draft Mode)
```
coworker.run({
operation: 'takeone',
from: 'Customer',
options: { render: true, draft: true } // ← Add draft
})
→ Creates ONE run:
{
operation: 'takeone',
input: { query params },
input.data: {}, // ← Empty accumulator
output: { data: [customer doc] },
options: { draft: true },
status: 'completed'
}
```
### Phase 2: Field Edit (NO New Run - Accumulation)
```
User types in field →
run.input.data.website = 'http://example.com/'
→ SAME run object:
{
operation: 'takeone', // ← Same
input: { query params },
input.data: { website: 'http://example.com/' }, // ← Accumulated
output: { data: [customer doc] }, // ← Unchanged
options: { draft: true }, // ← Still draft
status: 'completed'
}
→ Re-render (optional, based on render flag)
```
### Phase 3: Save (Child Run - Write Operation)
```
User clicks Save →
await run.save()
→ Creates CHILD run:
{
operation: 'update', // ← DIFFERENT operation
parent_run_id: 'run-original', // ← Has parent
source_doctype: 'Customer',
input: {
name: 'Jim Vorough',
website: 'http://example.com/',
...merged data
},
options: { draft: false } // ← Immediate execution
}🎯 Key Principles Violated
| Principle | What Should Happen | What's Happening |
|---|---|---|
| Single run per document operation | One 'takeone' run stays open | New 'takeone' run created |
| Input accumulation | Changes go to run.input.data | New run with separate input |
| Draft mode during editing | draft: true while editing | draft: false |
| Parent-child for writes | Save creates child 'update' | No parent relationship |
| Operation changes on save | takeone → update on save | takeone → takeone? |
💡 Root Cause Question
Where in your code is this new run being triggered?
Look for:
- Field component onChange handlers
- Auto-save logic
- Re-render triggers that call run()
- Event listeners on form inputs
The architecture requires:
- One run object for the document
- Field edits accumulate in that run's input.data
- No new runs until save is called
- Save creates child with different operation type
🔧 Questions to Diagnose
- In your FieldComponent, what does onChange actually do?
- Does it call coworker.run()?
- Does it just write to run.doc (which should point to run.input.data)?
- Is there auto-save logic running?
- Debounced save after field changes?
- Watching for changes and triggering runs?
- What triggers creation of this second run?
- User action?
- System automatic?
- Re-render side effect?
- Why is operation 'takeone' again instead of 'update'?
- Is the field component copying parent's operation?
- Should it be creating an 'update' operation?
The fact that you're seeing a second 'takeone' run suggests field edits are invoking run() when they shouldn't be - they should just accumulate in the existing run's input.data.
Ah! Now I can see the issue. Let me analyze this code:
const handleChange = (e) => {
const newValue = e.target.value;
setLocalValue(newValue);
clearTimeout(debounceTimerRef.current);
debounceTimerRef.current = setTimeout(() => {
run.input[field.fieldname] = newValue; // ← Writing to run.input directly
coworker.controller.autoSave(run);
}, 300);
};The issues:
- Writing to wrong location:
run.input[field.fieldname] = newValue- Should be:
run.input.data[field.fieldname] = newValue(in draft mode) - OR use the getter:
run.doc[field.fieldname] = newValue
- Should be:
- autoSave() is being called which then calls
save()which creates a CHILD run - The child run that's created by
save()has operation from the parent (takeone) copied somehow
Let me trace through what's happening:
- User types in field
- After 300ms debounce:
run.input[field.fieldname] = newValue- This writes to the input object but at the wrong level
coworker.controller.autoSave(run)is called- autoSave checks if complete, then calls
save() save()creates a child run with merged data- But somehow the child operation is 'takeone' instead of 'update'
Looking at the controller code they shared earlier:
async save(run) {
// ...
const saveRun = await run.child({
operation: isNew ? "create" : "update", // Should be 'update'
// ...
});
}So the child SHOULD have operation 'update', not 'takeone'.
I'm investigating a potential source of the operation mismatch. The run object's structure suggests something unusual is happening during the save process. The operation might be incorrectly propagating or being set at an unexpected point in the workflow. I need to trace how the 'takeone' operation is being preserved or mistakenly applied to the child run.
The sequence of events seems critical: field changes trigger an autosave, which creates a child run with an 'update' operation. However, a new run with 'takeone' mysteriously appears, potentially due to a re-render or incorrect input structure. The core issue might stem from how data is being written to the run's input - possibly targeting the wrong nested location.
🎯 Found the Issues
Looking at your FieldData component, I can see what's breaking the architecture:
❌ Issue 1: Writing to Wrong Location
run.input[field.fieldname] = newValue;
```
**Problem:** You're writing to `run.input` directly, not `run.input.data`
**Expected structure:**
```
run.input = {
query: { take: 1 }, // ← Query params (static)
data: { // ← Field accumulator (dynamic)
website: 'value'
}
}
```
**What you're creating:**
```
run.input = {
query: { take: 1 },
website: 'value' // ← Mixed with query params! Wrong level!
}This is why your run object shows:
"input": {
"website": "http://example.com/" // ← At wrong level
}❌ Issue 2: autoSave Creates Child Run
coworker.controller.autoSave(run);
```
**This triggers:**
1. `autoSave()` checks if complete
2. Calls `save()`
3. `save()` creates **child run** with operation 'update'
4. Child executes and writes to DB
**But you're seeing a run with operation 'takeone'** - this suggests either:
- Child run is copying parent's operation incorrectly
- OR a re-render is triggering NEW 'takeone' operation
- OR getCurrentRun() is returning wrong run
---
## 🤔 **Architectural Question: Should autoSave Create Child Runs?**
With your document-centric model, two options:
### **Option A: Draft Accumulation Only (No Child)**
```
Field change →
Write to run.input.data →
Validate (optional) →
Update UI state →
NO new run, NO child run
Save button clicked →
Create child 'update' run →
Execute and save to DB
```
**Benefit:** One document operation, clear lifecycle
**Drawback:** Manual save required
### **Option B: Auto-Save Creates Child (Current)**
```
Field change →
Write to run.input.data →
Debounced autoSave →
Create child 'update' run →
Save to DBBenefit: Automatic persistence Drawback: Multiple child runs for same document edit session
💡 The Core Design Question
In document-centric architecture, what does "draft mode" mean?
Interpretation 1: "Draft = Accumulation Phase"
- Draft mode means: collecting input, not executing yet
- autoSave would end draft mode (transition to execution)
- Each save creates child run and updates DB
- Multiple children for one document edit session
Interpretation 2: "Draft = Local-Only Editing"
- Draft mode means: all changes stay local until explicit save
- autoSave is disabled or just validates
- Only manual save() creates child run
- One child per document edit session
🎯 What's Actually Happening vs What Should Happen
| Event | Current Behavior | Document-Centric Ideal |
|---|---|---|
| Open form | Run 'takeone' → status completed | ✅ Same |
| Field edit | Write to run.input (wrong level) | Write to run.input.data |
| After 300ms | autoSave → child 'update' run → DB write | Accumulate only (no child) OR child 'update' (not 'takeone') |
| Save button | Another save? | First/final child 'update' run |
❓ Key Questions for Your Architecture
- Should draft mode allow auto-save at all?
- If yes: Each auto-save creates child run → multiple children OK?
- If no: Draft = accumulate only, save button required
- Why is the new run showing operation 'takeone'?
- Should be 'update' (from save's child creation)
- Is there re-render calling original operation again?
- Is getCurrentRun() returning wrong run?
- What does "document operation stays open" mean?
- Parent 'takeone' stays as context holder?
- Children 'update' execute actual DB writes?
- OR only one run total until save clicked?
- Should input.data be initialized?
- In run(), when draft: true, do you initialize run.input.data = {}?
- Or does field component create it first time?
✅ My Assessment
Your architecture CAN work with auto-save, but requires:
- Fix write location:
run.input.data[field]notrun.input[field] - Clarify parent-child relationship: Parent 'takeone' holds context, children 'update' save changes
- Ensure child operation is correct: save() should create 'update', not 'takeone'
- Track active run properly: getCurrentRun() should return parent during editing, show children in history
The run object you showed with operation 'takeone' and input at wrong level suggests both issues (1) and (3) are present.
Oh! This is a HUGE insight from Denis. He's saying:
Current thinking: Field edit writes to existing run's input accumulator His actual architecture: Field edit should trigger a NEW run
His principle: "All components are RESULT of run" - meaning components don't mutate runs, they are RENDERED BY runs.
So the flow should be:
- Initial run → renders form
- User edits field → NEW run (not mutation of old run)
- New run → renders updated form
- And so on...
This is more like:
- React's immutable state model
- Event sourcing pattern
- Each interaction is a new operation
The question is: What operation type? How does the new run relate to the old one?
Let me think about this architecturally:
Model 1: Each field edit is a new document operation
Run 1: operation='takeone' → renders form with original data
Run 2: operation='update_draft' → field edited, new run created
Run 3: operation='update_draft' → another field edited
Run 4: operation='update' → final saveBut this conflicts with "document-centric" - these aren't separate document operations.
Model 2: Parent operation continues, creates child runs for field changes
Run 1: operation='takeone', draft=true → renders form
└─ Run 1.1: operation='field_change', parent=Run1 → field edit
└─ Run 1.2: operation='field_change', parent=Run1 → another field
└─ Run 1.3: operation='update', parent=Run1 → final saveBut Denis said "NEW RUN should be initiated" - not child, but NEW.
Model 3: Each state is a new run that replaces previous
Run 1: takeone → data=original → renders form
Run 2: takeone → data=original+field1 → renders updated form
Run 3: takeone → data=original+field1+field2 → renders updated form
Run 4: update → saves to DBI see a pattern emerging where each field interaction creates a new run, progressively building state through immutable transformations. The key insight is treating every form interaction as a discrete, atomic operation that generates a completely new run state, rather than mutating an existing one.
The architecture suggests a pure functional approach where form state evolves through a series of immutable snapshots, each representing a distinct moment in the user's interaction journey.
The critical design question centers on how to trigger these incremental state updates - whether through direct component method calls, event-based mechanisms, or specialized draft update operations. Each approach offers unique tradeoffs in coupling and flexibility.
I'm exploring a functional model where form interactions generate a sequence of runs: an initial context-establishing run, followed by draft runs capturing partial edits, culminating in a final database write. This approach treats each edit as a discrete, traceable operation with clear lineage through parent run relationships.
The core architectural challenge involves maintaining immutability while supporting fluid, incremental user interactions. By modeling form state as a progressive series of runs, we create a transparent, auditable editing process that preserves the integrity of the underlying data model.
The proposed strategy involves three key run types: selection, drafting, and final update - each representing a distinct phase of the user's interaction with the form.
💡 Profound Architectural Insight
You've identified the core tension in your architecture:
"All components are RESULT of run - input → execute → save → render"
This means:
- Components are READ-ONLY views of run outputs
- Components DON'T mutate runs
- New state = NEW run
🎯 The Key Question
"How should field edit trigger a NEW run while maintaining document context?"
🤔 Three Architectural Models
Model 1: Parent-Child (Draft as Children)
Run 1 (parent): operation='takeone', draft=true
└─ Run 1.1 (child): operation='draft_update', field='website'
└─ Run 1.2 (child): operation='draft_update', field='customer_name'
└─ Run 1.3 (child): operation='update', saves to DB
Parent holds context
Children are incremental changes
Each child is a new runCharacteristics:
- Parent run stays open (status='draft' or 'in_progress')
- Each field edit creates child run
- Parent-child relationship maintains context
- Final save is also a child
Question: What happens to parent's input and output?
- Does parent accumulate children's changes?
- Or children are independent and parent just orchestrates?
Model 2: Linked Sequence (State Chain)
Run 1: operation='takeone' → output={original data}
↓ (triggers)
Run 2: operation='takeone' → input={website: 'new'} → output={merged data}
↓ (triggers)
Run 3: operation='takeone' → input={website: 'new', name: 'new2'} → output={merged data}
↓ (triggers)
Run 4: operation='update' → saves to DBCharacteristics:
- Each state is a new run
- Each run is immutable
- New run references previous run
- Sequence forms a chain
Question: All operations are 'takeone' until final save?
- Does 'takeone' in draft mode mean "take current accumulated state"?
- How does new run know about previous edits?
Model 3: Single Run, Operation Transitions
Run 1: operation='takeone', status='executing'
↓ (loads data)
Run 1: operation='takeone', status='draft' ← Transitions to draft mode
↓ (field edits accumulate in this state)
Run 1: operation='update', status='saving' ← Operation changes
↓ (saves to DB)
Run 1: operation='update', status='completed'Characteristics:
- Single run object throughout
- Operation can transition (takeone → update)
- Status reflects lifecycle phase
- Input accumulates within same run
But this violates: "Components don't mutate runs"
🎯 My Understanding of Your Architecture
Based on "NEW RUN should be initiated", I believe you want Model 1 or Model 2.
The question is: What triggers the new run, and what's the relationship?
💭 Critical Design Decisions
Decision 1: Who triggers the new run?
Option A: FieldComponent calls run() directly
onChange: () => {
coworker.run({
operation: 'draft_change',
parent_run_id: currentRun.name,
source_doctype: 'Customer',
input: { [field.fieldname]: value }
});
}Option B: FieldComponent emits event, orchestrator creates run
onChange: () => {
coworker.emit('field_changed', {
runId: currentRun.name,
field: field.fieldname,
value: value
});
}
// In orchestrator:
coworker.on('field_changed', (event) => {
coworker.run({ ... });
});Option C: autoSave is the trigger mechanism
onChange: () => {
// Write to temporary buffer
temporaryState[field] = value;
// Debounced
setTimeout(() => {
coworker.run({
operation: 'draft_update',
parent_run_id: currentRun.name,
input: temporaryState
});
}, 300);
}
```
---
### **Decision 2: What operation type for field edits?**
**Option A: Same operation continues ('takeone')**
- Each edit is a new 'takeone' operation
- 'takeone' in draft mode = "viewing current accumulated state"
- Builds a chain: takeone → takeone → takeone → update
**Option B: Different operation for drafting ('draft_update')**
- Field edits use special 'draft_update' operation
- Parent 'takeone' provides context
- Children 'draft_update' accumulate changes
- Final 'update' saves to DB
**Option C: Transitions within operation ('draft' status)**
- Not a new operation, but status change
- But this means mutation (conflicts with immutability)
---
### **Decision 3: Where does accumulated state live?**
**Option A: In parent run's input.data**
- Children write to parent's input.data
- Parent holds accumulated state
- Children are ephemeral events
**Option B: Each child run is a snapshot**
- Child run has full accumulated state in its output
- Each run is complete picture at that moment
- Chain of immutable states
**Option C: Separate state object**
- Runs don't hold accumulated draft state
- CoworkerState or similar holds draft
- Runs just execute operations
---
## 🎯 **My Recommendation for Your Architecture**
Given "components are RESULT of run" and "NEW RUN should be initiated":
```
Model: Parent-Child with State Snapshots
Run 1: operation='takeone', status='completed', draft=false
Purpose: Load original document
Output: { data: [original] }
Renders: MainForm
[User starts editing - parent transitions or new parent created]
Run 2: operation='takeone', status='draft', draft=true, parent=Run1
Purpose: Editing session context
Input: { data: {} } ← Empty accumulator
Output: { data: [original] } ← Unchanged from parent
Renders: MainForm in edit mode
[User edits field]
Run 2.1: operation='field_change', parent=Run2
Purpose: Record field change
Input: { field: 'website', value: 'http://example.com' }
Output: { data: [original + website] } ← Merged snapshot
Updates: Run2.input.data
Renders: MainForm with new value
[User edits another field]
Run 2.2: operation='field_change', parent=Run2
Purpose: Record another change
Input: { field: 'name', value: 'New Name' }
Output: { data: [original + website + name] }
Updates: Run2.input.data
Renders: MainForm with both changes
[User clicks Save]
Run 2.3: operation='update', parent=Run2
Purpose: Persist to DB
Input: Run2.input.data (accumulated)
Output: { data: [saved] }
Executes: DB write
Updates: Run2.output
Renders: MainForm with saved stateKey aspects:
- Run 2 is parent "editing session"
- Run 2.1, 2.2, etc. are field change events (children)
- Each child creates immutable snapshot
- Parent accumulates in input.data
- Children update parent and render
- No mutation - each run is new
Does this align with your vision?
Let me analyze what's happening when the user edits a field based on the code provided:
- User edits field in FieldData component
- FieldData does:
run.input[field.fieldname] = newValue;
coworker.controller.autoSave(run);- autoSave checks conditions and calls save()
- save() does:
const saveRun = await run.child({
operation: isNew ? "create" : "update",
source_doctype: doctype,
target_doctype: doctype,
input: merged,
query: isNew ? undefined : { where: { name: merged.name } },
options: {
includeSchema: false,
},
});So yes, a NEW run is created via run.child(). This child run:
- Has operation 'update' (not 'takeone')
- Should have parent_run_id set (via child() method)
- Gets merged input (original + delta)
But the user is seeing a run with operation 'takeone' in their example. This suggests either:
- The child() method is not working correctly
- OR there's another mechanism triggering runs
- OR getCurrentRun() is returning the wrong run
The user is asking if the new run is invoked "without accurate context of previous run". Looking at the code:
run.child()is called - this SHOULD maintain parent context- But we need to see what child() actually does
- The merged data includes original, so context IS passed via data
- But parent_run_id should link them
The architectural question the user is raising: Is this the right way? Should field edits trigger child runs via save()?
The user's concern: "NEW RUN should be initiated" but "without accurate context" suggests the parent-child relationship might not be properly maintained or the context isn't being passed correctly.
✅ Yes, Exactly - You've Found the Issue
When field is edited:
// FieldData component
run.input[field.fieldname] = newValue;
coworker.controller.autoSave(run);
↓
// autoSave calls save()
await this.save(run);
↓
// save() creates NEW run via child()
const saveRun = await run.child({
operation: isNew ? "create" : "update",
source_doctype: doctype,
target_doctype: doctype,
input: merged,
options: { includeSchema: false }
});🎯 The Context Problem
What Context IS Passed:
const merged = { ...original, ...delta };
// ↑ Data context (original merged with changes)
input: merged
// ↑ New run gets merged dataWhat Context is NOT Passed:
// Missing in child() call:
parent_run_id: run.name // ← Should be set by child() method
flow_id: run.flow_id // ← Editing session context
step_id: ??? // ← Where in editing flow are we?
// Also missing:
- Which field(s) changed?
- Why this run was created (auto-save vs manual save)?
- History of previous edits in this session?🤔 Key Architectural Questions
Question 1: Should child() automatically set parent context?
What child() SHOULD do:
run.child = function(childOp) {
return coworker.run({
...childOp,
options: {
...childOp.options,
parentRunId: this.name, // ← Auto-set parent
flowId: this.flow_id, // ← Inherit flow
// Inherit other context?
}
});
}
```
**Does your child() do this?** We need to see the implementation.
---
### Question 2: **What does "accurate context" mean for your architecture?**
**Option A: Execution Context Only**
```
Child run knows:
- Parent run ID
- Data to save (merged)
- Operation to perform
Child run does NOT need:
- Full editing history
- Which fields changed
- Why save was triggered
```
**Option B: Full Editing Session Context**
```
Child run knows:
- Parent run ID
- Flow ID (editing session)
- Step ID (which edit in sequence)
- Field change history
- Original vs current state
- Validation state
- User who made changesQuestion 3: Should parent run track child runs?
Currently:
// Parent run
parent_run_id: null
child_run_ids: [] // ← Empty or not updated?
// Child run (save)
parent_run_id: ??? // ← Should reference parentShould it be:
// Parent run (takeone - editing session)
parent_run_id: null
child_run_ids: ["run-save-1", "run-save-2"] // ← Track all saves
// Child run (auto-save 1)
parent_run_id: "run-takeone-parent"
step_id: "edit-1"
// Child run (auto-save 2)
parent_run_id: "run-takeone-parent"
step_id: "edit-2"
```
---
## 💡 **What You're Discovering**
**The problem isn't that a new run is created** - that aligns with your architecture.
**The problem is: The new run is disconnected from editing context.**
It's like taking a photo (child run) without recording:
- Where you were standing (parent context)
- Why you took the photo (auto-save vs manual)
- What changed since last photo (delta tracking)
---
## 🎯 **Two Possible Solutions**
### **Solution A: Enhance child() to preserve full context**
```
Child inherits from parent:
- parent_run_id (automatic)
- flow_id (editing session)
- All metadata needed to reconstruct history
Benefit: Full traceability
Drawback: Complex context management
```
### **Solution B: Parent stays open, accumulates children**
```
Parent run:
- status: 'editing' (not 'completed')
- child_run_ids: [all auto-save runs]
- input.data: accumulates changes
- When done editing → status: 'completed'
Children:
- Just DB write operations
- Parent holds true editing state
Benefit: Clear editing session boundaries
Drawback: Parent run lifecycle management❓ Critical Question
In the run object you showed earlier with operation 'takeone' - is that:
A. The original parent run (still showing as current)? B. A child run that incorrectly copied parent's operation? C. A completely new sibling run (not a child)?
Because save() creates child with operation 'update', but you're seeing 'takeone'. This suggests:
- Either child() method isn't setting operation correctly
- OR getCurrentRun() is returning parent instead of child
- OR there's another mechanism creating 'takeone' runs
Can you check: What does your run.child() method implementation look like?
This is a brilliant observation. The user is identifying a fundamental architectural gap:
Programmatic invocation (API-like):
- Clear:
coworker.run({ operation: 'update', doctype: 'Customer', input: {...} }) - Context is explicit in the call
- Straightforward
UI invocation (user actions):
- User clicks button
- User edits field
- User submits form
- How do these map to run() operations?
- What context needs to be passed?
The user is asking for an architectural framework for:
- UI Action → Operation Mapping: What operation should each UI action trigger?
- Context Propagation: How does context flow from UI actions through to run()?
This is the missing piece in the architecture - the bridge between user interactions and the run() pipeline.
Let me think about this systematically:
UI Action Types:
- Open/View: Click to open form, view list
- Edit: Type in field, change value
- Save: Click save button
- Delete: Click delete
- Submit/Cancel: For submittable docs
- Navigate: Move between forms
Context that needs to flow:
- Which document (name/id)
- Which doctype
- Which field (for edits)
- Parent run (for maintaining session)
- User intent (auto-save vs manual save)
- UI state (which form, which tab)
The user is asking for a design pattern or architecture that handles this mapping systematically, not ad-hoc.
💡 Brilliant Insight - You've Identified the Missing Architectural Layer
You're right. There's a fundamental difference between:
📊 Two Invocation Patterns
Pattern 1: Programmatic (Server/API style)
coworker.run({
operation: 'update',
source_doctype: 'Customer',
input: { name: 'CUST-001', website: 'example.com' },
query: { where: { name: 'CUST-001' }}
})Context is explicit: Everything needed is in the parameters.
Pattern 2: UI-Driven (User interaction)
// User types in field
onChange: (e) => {
// ❓ What operation?
// ❓ What context from parent?
// ❓ How to map this action?
??? → coworker.run({ ??? })
}
```
**Context is implicit:** Exists in UI state, parent runs, component props.
---
## 🎯 **The Core Problem**
**You need a systematic mapping layer:**
```
UI Action → Operation Mapping → Context Extraction → run() InvocationWithout this, every component invents its own mapping, leading to:
- Inconsistent operation choices
- Lost context
- Broken parent-child relationships
- Duplicate/incorrect runs
💡 Architectural Solution: UI Action Mapper
Layer Purpose:
Translate user actions into properly contextualized run() calls
🏗️ Design Pattern
Component Layer (Dumb)
// Component just declares WHAT happened
const FieldData = ({ field, run }) => {
const handleChange = (e) => {
coworker.handleUIAction({
action: 'field_change',
context: {
parentRun: run,
field: field,
value: e.target.value
}
});
};
}Mapper Layer (Smart)
coworker.handleUIAction = async function(uiAction) {
const { action, context } = uiAction;
// Look up mapping
const mapping = this._uiActionMappings[action];
if (!mapping) {
console.error(`No mapping for UI action: ${action}`);
return;
}
// Build run() call with proper context
const runParams = mapping.buildRunParams(context);
// Execute
return await this.run(runParams);
}Configuration Layer (Declarative)
coworker._uiActionMappings = {
field_change: {
operation: 'draft_update',
buildRunParams: (ctx) => ({
operation: 'draft_update',
source_doctype: ctx.parentRun.source_doctype,
input: {
[ctx.field.fieldname]: ctx.value
},
options: {
parentRunId: ctx.parentRun.name,
flowId: ctx.parentRun.flow_id,
autoSave: true,
debounce: 300
}
})
},
save_button: {
operation: 'update',
buildRunParams: (ctx) => ({
operation: 'update',
source_doctype: ctx.parentRun.source_doctype,
input: ctx.parentRun.input.data,
query: { where: { name: ctx.parentRun.doc.name }},
options: {
parentRunId: ctx.parentRun.name,
flowId: ctx.parentRun.flow_id
}
})
},
open_form: {
operation: 'takeone',
buildRunParams: (ctx) => ({
operation: 'takeone',
source_doctype: ctx.doctype,
query: { where: { name: ctx.docname }},
options: {
draft: true,
render: true
}
})
},
delete_button: {
operation: 'delete',
buildRunParams: (ctx) => ({
operation: 'delete',
source_doctype: ctx.parentRun.source_doctype,
query: { where: { name: ctx.parentRun.doc.name }},
options: {
parentRunId: ctx.parentRun.name
}
})
}
}
```
---
## 🎯 **Benefits of This Pattern**
| Aspect | Benefit |
|--------|---------|
| **Centralized mapping** | All UI→Operation logic in one place |
| **Consistent context** | Every action gets correct parent/flow context |
| **Declarative** | Easy to see all UI actions and their mappings |
| **Testable** | Can test mappings independently |
| **Extensible** | Add new actions without touching components |
| **Debuggable** | Single point to log/trace UI actions |
---
## 🔄 **Complete Flow Example**
```
1. User types in field
↓
2. FieldData.onChange fires
↓
3. handleUIAction({ action: 'field_change', context: {...} })
↓
4. Mapper looks up 'field_change' mapping
↓
5. buildRunParams extracts:
- parentRunId from context.parentRun.name
- flowId from context.parentRun.flow_id
- doctype from context.parentRun.source_doctype
- field/value from context
↓
6. coworker.run({
operation: 'draft_update',
source_doctype: 'Customer',
input: { website: 'example.com' },
options: {
parentRunId: 'run-abc123',
flowId: 'flow-xyz',
autoSave: true
}
})
↓
7. New run created with full context💭 Questions This Pattern Answers
Q: "What operation for field edit?"
A: Defined in mapping: field_change → draft_update
Q: "How to pass parent context?"
A: Mapper extracts from context.parentRun automatically
Q: "When to auto-save vs manual save?"
A: Different actions: field_change (auto) vs save_button (manual)
Q: "How to track editing session?"
A: flowId propagated through all child runs
Q: "Should field edit create new run?"
A: Mapping decides: can be new run OR accumulation in parent
🎯 Two Architectural Choices
With this mapper pattern, you can choose behavior declaratively:
Choice A: Field Edit Creates New Run
field_change: {
operation: 'draft_update', // ← New child run
buildRunParams: (ctx) => ({ ... })
}Choice B: Field Edit Accumulates in Parent
field_change: {
operation: null, // ← No new run
handler: (ctx) => {
// Accumulate in parent
ctx.parentRun.input.data[ctx.field.fieldname] = ctx.value;
// Validate
coworker.controller.validateField(ctx.parentRun, ctx.field, ctx.value);
// Re-render
coworker._render(ctx.parentRun);
}
}✅ Does This Address Your Question?
The mapper layer provides:
- ✅ Systematic UI Action → Operation mapping
- ✅ Automatic context propagation
- ✅ Centralized behavior control
- ✅ Clear separation: Components → Mapper → run()
Is this the architectural pattern you were looking for? Or do you see a different approach?
The user is asking for architectural advice on how to handle the mapping between:
- Atomic field components (in MainForm)
- Row components (in MainGrid)
- Their parent operations/contexts
They already have:
- Schema-based system with global flags (is_submittable, docstatus, _autosave)
- Field-level rules (system field rules, field type handlers, custom rules)
- Operation configs (draft, validate, requiresSchema, etc.)
- View configs (list/form/chat → components)
The key insight they're getting at:
- Parent components (Main*) have full context (schema, operation, run)
- Atomic components (FieldData, FieldCheck, etc.) need to know how to interact with parent context
- Need systematic mapping of: "when user does X in atomic component → what happens in context of parent operation"
They're asking: Should there be config that defines atomic component behavior based on parent context?
Let me think about this architecturally without code...
The hierarchy is:
Run (operation + schema + flags)
↓
Main Component (MainForm/MainGrid) - has full context
↓
Atomic Components (FieldData, GridRow) - need context-aware behaviorThe question is: How do atomic components know what to do based on parent context?
Options:
- Config-driven behavior mapping
- Props drilling from parent
- Context API pattern
- Event system with parent as handler
I think they're leaning toward config-driven because they already have:
- Operation config (draft, validate, etc.)
- Field rules config (system rules, handlers)
- View config (component mappings)
So the missing piece is: "Field interaction config based on parent operation/schema context"
This would define things like:
- When field changes in a draft form → accumulate or create new run?
- When field changes in read-only view → ignore or error?
- When field changes in submittable doc with docstatus=1 → block or allow?
- When field changes with _autosave=1 → debounced save or immediate?
I see a comprehensive configuration approach emerging. The config would essentially create a decision matrix for atomic components, mapping their behavior to the parent context's state. This allows for granular, declarative control over how fields interact across different document states and operations.
🎯 Excellent Architectural Direction
You've identified the missing config layer. Let me map out the conceptual model:
📊 Current Config Hierarchy
Level 1: GLOBAL (Schema-level)
├─ is_submittable
├─ docstatus
├─ _autosave
└─ istable
Level 2: OPERATION (What's happening)
├─ draft: true/false
├─ validate: true/false
├─ requiresSchema
└─ type: read/write
Level 3: VIEW (How it's displayed)
├─ component: MainForm/MainGrid
├─ container: main_container
└─ render: true
Level 4: FIELD SYSTEM (Data processing)
├─ System field rules (name, owner, etc.)
├─ Field type handlers (Data, Link, Check, etc.)
└─ Custom rules
Level 5: ??? ATOMIC COMPONENT BEHAVIOR ???💡 The Missing Layer: Field Interaction Config
What it should define:
1. Interaction Mode Matrix
Based on parent context, what can user do with atomic component?
Context Factors:
- Parent operation (takeone, create, update)
- Draft mode (true/false)
- Schema flags (is_submittable, _autosave)
- Document state (docstatus, __islocal)
- Field properties (read_only, depends_on)
Determines:
- Can edit? (enabled/disabled)
- Edit triggers what? (accumulate/save/validate/error)
- Edit creates what? (nothing/child run/new sibling)
- Edit timing? (immediate/debounced/on-blur)🏗️ Conceptual Config Structure
Interaction Profiles
Define named profiles that combine behavior rules:
Profile: "draft_form_editable"
├─ When: operation.draft=true AND schema.docstatus=0
├─ Field interaction:
│ ├─ onChange: accumulate in run.input.data
│ ├─ Validation: real-time (non-blocking)
│ ├─ Save trigger: auto (if _autosave=1)
│ └─ New run: no (accumulation only)
Profile: "submitted_form_readonly"
├─ When: schema.is_submittable=1 AND doc.docstatus=1
├─ Field interaction:
│ ├─ onChange: blocked (unless allow_on_submit)
│ ├─ Validation: none
│ ├─ Save trigger: none
│ └─ New run: no
Profile: "new_document_creation"
├─ When: operation=create AND doc.__islocal=true
├─ Field interaction:
│ ├─ onChange: accumulate in run.input.data
│ ├─ Validation: on-blur
│ ├─ Save trigger: manual only
│ └─ New run: no
Profile: "grid_inline_edit"
├─ When: component=MainGrid AND field.in_list_view=true
├─ Field interaction:
│ ├─ onChange: immediate save per row
│ ├─ Validation: blocking
│ ├─ Save trigger: immediate
│ └─ New run: yes (update operation per row)🎯 Atomic Component Decision Logic
Instead of atomic components deciding behavior, they:
- Receive profile from parent:
MainForm passes: {
interactionProfile: "draft_form_editable",
parentContext: { run, schema, operation }
}- Look up behavior in config:
Profile determines:
- Should I be enabled?
- What happens on change?
- Do I validate?
- Do I trigger save?- Delegate to parent's action handler:
Component doesn't call run() directly
Component calls: parentContext.handleFieldChange(field, value)
Parent uses profile to decide: accumulate vs new run vs error🔄 Flow Example
Scenario: Edit field in draft form
1. User opens form
→ run({ operation: 'takeone', draft: true })
→ MainForm receives run with schema
2. MainForm evaluates context:
- operation.draft = true
- schema.is_submittable = 1
- doc.docstatus = 0
- schema._autosave = 1
3. MainForm selects profile: "draft_submittable_autosave"
4. MainForm passes to FieldData:
- interactionProfile config
- handleFieldChange callback
5. User types in field:
- FieldData calls: handleFieldChange(field, value)
- MainForm looks up profile behavior:
→ onChange: "accumulate"
→ validation: "real-time"
→ saveTrigger: "debounced-300ms"
6. MainForm executes behavior:
- Write to run.input.data[field] (accumulation)
- Validate field (real-time, non-blocking)
- Schedule debounced save (will create child run)
7. After 300ms (if no more changes):
- MainForm calls controller.autoSave(run)
- Creates child run({ operation: 'update' })
- Child has parent_run_id🎯 Key Architectural Principles
1. Atomic Components are Dumb
- Just render UI
- Fire callbacks to parent
- Don't know about operations, schemas, or runs
2. Main Components are Smart Orchestrators
- Have full context (run, schema, operation)
- Evaluate which profile applies
- Handle callbacks based on profile behavior
- Decide when to create new runs
3. Profiles are Declarative
- Config-driven behavior matrix
- Combine multiple context factors
- Define clear interaction rules
- Centralized and testable
4. Context Flows Down, Actions Flow Up
Context (down): Actions (up):
Main → Atomic Atomic → Main → run()💭 Benefits of This Model
| Benefit | How |
|---|---|
| Consistency | Same field behaves correctly across contexts |
| Maintainability | Behavior changes in config, not scattered in components |
| Testability | Profile selection logic can be tested independently |
| Flexibility | New profiles without changing components |
| Traceability | Clear mapping from context → behavior → action |
| Separation | UI separated from business logic |
🤔 Questions for Your Design
Q1: Profile Selection
How does MainForm select which profile applies?
- Single profile per form state?
- Field-level profiles (different fields different rules)?
- Cascading rules (global → schema → field)?
Q2: Profile Composition
Can profiles be composed/inherited?
Base profile: "editable_form"
+ Schema modifier: "submittable_rules"
+ Field modifier: "read_only_when_submitted"
= Final profile for specific field in specific stateQ3: New Run Decision
Who decides if action creates new run?
- Profile defines: "accumulate" vs "new_run"
- MainForm interprets profile and calls run() if needed
- Or separate action handler layer?
Q4: Grid vs Form
Do atomic components work same in both?
- Same FieldData in MainForm and MainGrid?
- But different profiles? (form=accumulate, grid=immediate-save)
- Or different components entirely?
✅ Recommended Config Structure
coworker._config = {
// Existing...
operations: { ... },
views: { ... },
fieldHandlers: { ... },
systemFieldRules: { ... },
// NEW:
interactionProfiles: {
draft_form: {
appliesWhen: {
operation_draft: true,
docstatus: 0,
component: 'MainForm'
},
fieldBehavior: {
onChange: 'accumulate',
validation: 'realtime',
saveTrigger: '_autosave_flag', // Check schema._autosave
debounceMs: 300
}
},
submitted_readonly: {
appliesWhen: {
is_submittable: true,
docstatus: 1
},
fieldBehavior: {
onChange: 'block', // Unless allow_on_submit
validation: 'none',
saveTrigger: 'never'
}
},
grid_inline: {
appliesWhen: {
component: 'MainGrid'
},
fieldBehavior: {
onChange: 'immediate_save',
validation: 'blocking',
saveTrigger: 'immediate',
createNewRun: true,
runOperation: 'update'
}
}
}
}🎯 Does This Align With Your Vision?
This completes your hierarchy:
- Global flags (schema)
- Operation config (behavior)
- View config (display)
- Field rules (processing)
- Interaction profiles (UI behavior based on context)
All atomic components work through profiles, Main components orchestrate based on context, and everything stays config-driven and declarative.
Is this the missing architectural piece you were identifying?
The user is showing me their run() code and asking about profile selection. They're making a crucial point:
"How does MainForm select which profile applies? it doesnt select ANYTHING. if it just the end of execution of run with render() and rendererig is from config"
This is a fundamental insight about their architecture. Let me understand what they're saying:
- run() resolves everything from config
- run() executes the operation
- If render flag is true, it renders the component
- The component (MainForm) is just a VIEW of the run result
So MainForm doesn't "select" anything - it RECEIVES the fully resolved run_doc which already has all the context it needs.
The profile selection shouldn't happen IN MainForm - it should happen:
- Either in run() during resolution
- Or MainForm just reads what profile to use from the run_doc itself
This is a declarative, data-driven approach where:
- run_doc carries ALL context (operation, schema, draft flag, options)
- Component just renders based on that context
- No "smart" selection logic in components
So the question becomes: Where in the pipeline does the interaction profile get determined and attached to run_doc?
Looking at their code:
- _resolveAll() resolves operation, doctype, view, component, container, options
- run_doc is constructed with all resolved values
- Component is rendered with run_doc
The profile should be resolved similarly and attached to run_doc so MainForm can just read it, not select it.
This is pure data-driven architecture - components are pure views.
💡 Ah! Critical Architectural Understanding
You're absolutely right. Let me reframe:
🎯 Your Architecture: Data-Driven, Not Logic-Driven
MainForm doesn't SELECT anything. It RECEIVES everything.
run() → resolves ALL context → renders component
Component is just VIEW of resolved context📊 The Real Flow
Phase 1: Resolution (in run())
Input:
- operation: 'takeone'
- doctype: 'Customer'
- options: { ... }
↓ _resolveAll()
Resolved:
- operation: 'takeone'
- source_doctype: 'Customer'
- view: 'form'
- component: 'MainForm'
- container: 'main_container'
- options: { render: true, draft: false }
↓ Construct run_doc
run_doc = {
operation: 'takeone',
source_doctype: 'Customer',
view: 'form',
component: 'MainForm',
options: { render: true, draft: false },
output: { schema: {...}, data: [...] }
}
↓ Execute through controller
↓ If options.render === true
↓ Render componentPhase 2: Rendering (MainForm receives)
MainForm receives: run_doc
MainForm doesn't decide anything
MainForm reads:
- run.operation
- run.options.draft
- run.output.schema
- run.output.data
MainForm renders based on what it receives🎯 So Where Should Interaction Profile Resolution Happen?
Answer: In _resolveAll() or during run_doc construction
Just like you resolve:
- operation → view → component
- doctype → source/target
You should also resolve:
- operation + schema flags → interactionProfile
🏗️ Extended Resolution in run()
_resolveAll() currently resolves:
├─ operation (from alias)
├─ doctype (from alias)
├─ view (from operation)
├─ component (from view)
├─ container (from view)
└─ options (from view config)
Should ALSO resolve:
└─ interactionProfile (from operation + schema flags)💡 Profile Resolution Logic
Option A: Resolve during _resolveAll()
Problem: Schema isn't loaded yet during resolution
Option B: Resolve after operation executes
After controller.execute() completes:
- Schema is in run_doc.output.schema
- Document is in run_doc.output.data
- NOW determine interaction profile based on:
- run_doc.operation
- run_doc.options.draft
- run_doc.output.schema.is_submittable
- run_doc.output.schema._autosave
- run_doc.output.data[0].docstatus
Option C: Resolve on-demand when rendering
Before calling _render():
// After execute, before render
if (mergedOptions.render) {
run_doc._interactionProfile = this._resolveInteractionProfile(run_doc);
this._render(run_doc);
}🎯 Recommended: Profile Resolution Function
_resolveInteractionProfile(run_doc) {
Inputs:
- run_doc.operation
- run_doc.options.draft
- run_doc.output.schema.* (all schema flags)
- run_doc.output.data[0].docstatus
- run_doc.component
Logic:
- Match against profile conditions in config
- Return profile name/config
Output:
- run_doc._interactionProfile = "draft_form_editable"
Then:
- MainForm receives run_doc with ._interactionProfile
- MainForm reads profile config
- MainForm configures all fields based on profile
}📋 Profile Config Structure
interactionProfiles: {
draft_form_editable: {
// Profile is selected when these conditions match
matches: {
component: 'MainForm',
draft: true,
docstatus: 0
},
// Profile defines field behavior
fieldBehavior: {
enabled: true,
onChange: 'accumulate', // Write to run.input.data
validation: 'realtime',
saveTrigger: 'auto', // Check _autosave flag
debounceMs: 300
}
},
submitted_form_readonly: {
matches: {
component: 'MainForm',
is_submittable: true,
docstatus: 1
},
fieldBehavior: {
enabled: false, // Unless field.allow_on_submit
onChange: 'blocked',
validation: 'none',
saveTrigger: 'never'
}
},
new_document_form: {
matches: {
component: 'MainForm',
operation: 'create',
__islocal: true
},
fieldBehavior: {
enabled: true,
onChange: 'accumulate',
validation: 'on-blur',
saveTrigger: 'manual'
}
}
}🔄 Complete Flow With Profile
1. coworker.run({ operation: 'takeone', doctype: 'Customer' })
2. _resolveAll()
→ operation: 'takeone'
→ view: 'form'
→ component: 'MainForm'
→ options: { render: true, draft: false }
3. controller.execute()
→ Fetches data
→ Loads schema
→ run_doc.output = { data: [...], schema: {...} }
4. _resolveInteractionProfile(run_doc)
→ Checks conditions:
- component: 'MainForm'
- draft: false
- schema.is_submittable: 1
- data[0].docstatus: 0
→ Matches: 'draft_form_editable'
→ run_doc._interactionProfile = 'draft_form_editable'
5. _render(run_doc)
→ Calls MainForm with run_doc
→ MainForm reads:
- run._interactionProfile
- config.interactionProfiles['draft_form_editable']
→ Configures all FieldComponents based on profile
6. FieldData receives:
- field definition (from schema)
- run (with all context including profile)
- profile behavior (from parent)
7. FieldData.onChange:
- Reads profile: onChange='accumulate'
- Writes to: run.input.data[field]
- Reads profile: saveTrigger='auto'
- Checks: schema._autosave
- Debounces: controller.autoSave(run)✅ Key Insight: Components Don't Decide, They Read
| Traditional Approach | Your Architecture |
|---|---|
| Component has logic | Component is pure view |
| Component decides behavior | run_doc carries behavior |
| Smart components | Dumb components |
| Logic scattered | Logic centralized in run() |
MainForm doesn't select profile. run() resolves profile and MainForm receives it.
🎯 Where to Add Profile Resolution
In your run() function, after controller.execute() and before _render():
// After execution
const result = await this.controller.execute(run_doc);
// Merge result into run_doc
run_doc.output = result.output;
run_doc.success = result.success;
run_doc.error = result.error;
// ✅ NEW: Resolve interaction profile (if rendering)
if (mergedOptions.render && run_doc.success) {
run_doc._interactionProfile = this._resolveInteractionProfile(run_doc);
}
// Render
if (mergedOptions.render) {
this._render(run_doc);
}Profile resolution happens in the pipeline, not in the component.
Does this align with your data-driven architecture?
The user is questioning why I said "Problem: Schema isn't loaded yet during resolution" when talking about resolving the interaction profile during _resolveAll().
Let me think about this... Looking at the user's code:
_resolveAll()happens at the BEGINNING ofrun()- before any execution- Schema loading happens DURING execution via
controller.execute() - The schema ends up in
run_doc.output.schemaAFTER execution
So my statement was correct - during _resolveAll(), the schema hasn't been loaded yet because that happens later during execution.
But wait - let me check the user's operation config:
takeone: {
type: "read",
draft: false,
requiresSchema: false, // ← This says schema not required!
validate: false,
fetchOriginals: false,
bypassController: false,
}Hmm, but the user also has requiresSchema: true for some operations like create/update.
And looking at the controller code:
if (opConfig.requiresSchema && !options.skipSchema) {
if (!run_doc.output) run_doc.output = {};
const doctype = run_doc.source_doctype || run_doc.target_doctype;
if (!run_doc.output.schema && doctype && doctype !== "Schema") {
const schema = await coworker.getSchema(doctype);
run_doc.output.schema = schema;
}
}So schema IS loaded during controller.execute() if requiresSchema is true.
But wait - maybe the user is questioning whether schema could be loaded EARLIER? Or maybe they're pointing out that schema COULD be loaded during _resolveAll() if we wanted?
Actually, looking at their field system code, they have this pattern:
// Fetch schema - check output first (if already fetched), then fetch if needed
let schema = run_doc.output?.schema;
if (!schema) {
schema = await this.getSchema(doctype);
run_doc._schema = schema;
}I see now that schema loading is more flexible than I initially assumed. The user's code demonstrates the ability to fetch schema proactively, suggesting that my original constraint about schema availability during resolution might be overly rigid. They're highlighting that schema could potentially be retrieved earlier in the process, challenging my assumption about when schema must be loaded.
The key insight is the potential for more dynamic schema handling, with the ability to fetch and prepare schema before operation execution if needed. This approach offers more flexibility in profile resolution and schema management.
You're right to question that. Let me reconsider:
🤔 Schema Loading Timeline
Looking at your code, schema CAN be loaded at different points:
Current Flow:
run() starts
↓
_resolveAll() (no schema yet)
↓
controller.execute()
↓
if (opConfig.requiresSchema) {
schema = await getSchema(doctype) ← Loads HERE
run_doc.output.schema = schema
}
↓
_render()But Schema COULD Be Loaded Earlier:
run() starts
↓
_resolveAll()
↓
if (need schema for profile resolution) {
schema = await getSchema(doctype) ← Load HERE
}
↓
resolve profile using schema
↓
controller.execute()
↓
_render()✅ You're Right - It's Not a "Problem"
Schema loading timing is flexible. If profile resolution needs schema flags, just load it during resolution:
_resolveAll() {
// ... existing resolution ...
// If we need schema for profile resolution:
if (needsInteractionProfile) {
const doctype = resolved.source_doctype || resolved.target_doctype;
resolved.schema = await this.getSchema(doctype);
}
// Now can resolve profile with schema available
resolved.interactionProfile = this._matchProfile({
component: resolved.component,
operation: resolved.operation,
draft: resolved.options.draft,
is_submittable: resolved.schema?.is_submittable,
// ... etc
});
return resolved;
}💡 The Real Question Is Design Choice
Not "can we" but "should we"?
Option A: Load Schema Early (During Resolution)
- Profile resolution has full context
- One place to resolve everything
- But: Extra DB call even if operation doesn't need schema
Option B: Load Schema During Execute (Current)
- Only load if operation needs it (requiresSchema flag)
- More efficient
- But: Profile resolution must wait until after execute
Option C: Conditional Early Loading
- Load schema during resolution ONLY if:
- Profile resolution is needed (render=true)
- AND profile needs schema flags
- Best of both worlds
🎯 So the Real Answer
Profile resolution can happen:
- During _resolveAll() - if you load schema there first
- After execute() - using already-loaded schema
- On-demand - lazy load when needed for rendering
There's no technical constraint. It's an architectural choice about when to pay the cost of schema loading.
My statement was wrong - it's not a "problem," it's just about where you want to sequence the schema fetch.
Which timing makes most sense for your architecture?
Ah, I see. The user is redirecting me. They don't want to discuss implementation details like when schema loads or where profiles are resolved. They want to focus on the bigger architectural question:
How should UI actions from atomic components (fields in MainForm, rows in MainGrid) trigger new run() invocations in a unified, consistent way?
The key insight they're driving at:
- Main* components (MainForm, MainGrid, etc.) are rendered as result of run()
- These Main* components contain atomic parts (fields, rows, buttons)
- When user acts on these atomic parts, it should trigger NEW run() invocations
- There needs to be a unified approach for how these UI actions map to operations
They're asking: What's the architectural pattern for atomic UI actions → new run() invocations, maintaining proper context and using the same config-driven approach?
Let me focus on the conceptual model of how UI actions should flow back into run() system.
🎯 Refocusing: UI Actions → New run() Invocations
You're asking about the architectural pattern for:
When user acts on parts of Main components → How should that trigger new run() calls?*
📊 The Core Challenge
Main* Component (Result of run)
├─ FieldData (atomic part)
├─ FieldCheck (atomic part)
├─ FieldLink (atomic part)
└─ SaveButton (atomic part)
User acts on atomic part → ??? → new run() invocationQuestion: What's the unified pattern for the ??? part?
💡 Conceptual Model
Current Pattern: run() → render Main component*
coworker.run({operation, doctype, ...})
→ resolve everything
→ execute
→ render MainForm/MainGrid/MainChatNew Pattern: Main component action → run()*
User clicks/types in MainForm
→ ???
→ coworker.run({???, ???, ...})
→ resolve everything
→ execute
→ render result🤔 Key Architectural Questions
Q1: What information flows from atomic action to run()?
When user edits field in MainForm:
- Parent run context?
- Current document state?
- Which field changed?
- New value?
- Operation to trigger?
Q2: Who decides what operation to invoke?
- Atomic component (FieldData decides)?
- Parent component (MainForm decides)?
- Config (action mapping defines)?
- Schema rules (field type determines)?
Q3: Should atomic actions always create new runs?
- Every field change = new run?
- Accumulate changes, then one run on save?
- Different behavior based on context?
Q4: How to maintain context chain?
Run 1 (takeone) → renders MainForm
User edits field
→ Run 2 (???)
How does Run 2 know about Run 1?
parent_run_id?
flow_id?
Something else?💭 Three Architectural Approaches
Approach A: Direct Action Mapping
Config defines:
atomic_action → operation mapping
Example:
field_change_in_form → 'draft_update' operation
field_change_in_grid → 'update' operation
save_button_click → 'update' operation
delete_button_click → 'delete' operation
Atomic component fires named action
Config maps action to operation
System calls run() with mapped operation + contextCharacteristics:
- Declarative mapping in config
- Components emit semantic actions
- Config translates to operations
Approach B: Parent Orchestration
Atomic components don't know about run()
Atomic components call parent methods
Parent (MainForm) decides operation
Example:
FieldData: onChange → parent.handleFieldChange(field, value)
MainForm.handleFieldChange → decides: accumulate or run()Characteristics:
- Parent has full context
- Parent decides when to invoke run()
- Components stay decoupled
Approach C: Event Bus Pattern
Atomic components emit domain events
Event bus routes to handlers
Handlers invoke run() with context
Example:
FieldData: onChange → emit('field.changed', {field, value, context})
EventHandler: on('field.changed') → decide operation → run()Characteristics:
- Loose coupling
- Observable pattern
- Handlers can be configured
🎯 The Unified Pattern You Need
Should combine:
- Semantic UI Actions (what user did, not how to handle)
- Context Passing (parent run, schema, document state)
- Config-Driven Mapping (action + context → operation)
- Consistent run() Invocation (same pipeline regardless of source)
💡 Conceptual Flow
User Action on Atomic Part
↓
Emit Semantic Action + Context
↓
Action Mapper (config-driven)
├─ Match action type
├─ Evaluate context (schema flags, doc state, parent run)
├─ Determine operation
└─ Build run() parameters
↓
coworker.run({
operation: (mapped),
parent_run_id: (from context),
flow_id: (from context),
doctype: (from context),
input: (extracted from action),
options: (from mapping rules)
})
↓
Same Resolution → Execute → Render Pipeline🤔 Design Decisions Needed
Decision 1: Granularity
- One action type per atomic component type?
- Or more granular (field_change_blur vs field_change_typing)?
Decision 2: Context Packaging
- What context must flow from parent to action?
- Minimal (just parent_run_id)?
- Full (entire parent run)?
Decision 3: Mapping Flexibility
- Static mapping (field_change always → draft_update)?
- Dynamic mapping (depends on schema flags, doc state)?
Decision 4: Invocation Timing
- Immediate (every action → run())?
- Batched (accumulate, then run())?
- Conditional (only if certain rules met)?
🎯 Key Insight
Your question is about:
"How to create a unified, config-driven pattern where UI actions from atomic components within Main components consistently and correctly invoke new run() calls with proper context inheritance?"*
This requires:
- Action vocabulary (semantic names for what happened)
- Context extraction (how to package parent state)
- Action mapping (config-driven translation)
- Run invocation (consistent parameters)
Is this the architectural pattern you want to design?
Ah! Now I understand the key insight:
- UI operations are on PARTS of Main* containers (fields, rows)
- These parts are ALWAYS schema-bound (either a field or row of fields)
- There's a gatekeeper/context check at the document level first
- Default actions based on document editability (docstatus, etc.)
- It SHOULD NOT be difficult - it should be straightforward because every atomic element maps directly to schema
The user is saying:
- Every atomic UI element is either:
- A schema field (in MainForm)
- A row (subset of schema fields) (in MainGrid)
- So the mapping is naturally defined by the schema itself
- The complexity people add with "action mappers" and "event buses" is overthinking
- Just check: is doc editable? → then field edit means "accumulate/update based on simple rules"
This is a schema-centric view where:
- Schema defines what fields exist
- Document state (docstatus) defines if editable
- Field type defines how to handle input
- That's it. No complex mapping needed.
Let me reformulate understanding this simplification.
💡 Ah! Much Clearer - Simplified Schema-Centric Model
You're saying: Stop overcomplicating it.
🎯 The Simple Truth
Every UI Atomic Element IS:
- A schema field (in MainForm)
- A row (subset of schema fields) (in MainGrid)
That's it. No abstract "actions" - just schema-defined parts.
🚪 The Gatekeeper Pattern
Step 1: Document-Level Check (Simple)
Is document editable?
├─ Check: docstatus === 0 (draft)
├─ Check: !read_only
├─ Check: user permissions
└─ Result: YES or NOIf NO → block all edits, done. If YES → proceed to field-level.
Step 2: Field-Level Check (Simple)
Is THIS field editable?
├─ Check: field.read_only
├─ Check: field.read_only_depends_on (evaluate)
├─ Check: field.allow_on_submit (if docstatus=1)
└─ Result: enabled or disabledStep 3: Default Action (Simple)
If field is enabled:
onChange → write to run.input.data[fieldname]
(that's the default)
If schema._autosave === 1:
debounce → controller.autoSave(run)
(creates child update run)
Done.✅ No Complex Mapping Needed
Why?
Because:
- UI element = schema field (direct mapping exists naturally)
- Document state = simple flags (docstatus, read_only)
- Default behavior = obvious (if editable → accumulate, if _autosave → save)
🏗️ The Actual Pattern
For MainForm (Field Edit):
User types in FieldData
↓
Gatekeeper:
1. Is doc editable? (docstatus === 0)
NO → field disabled, nothing happens
YES → continue
2. Is field editable? (field.read_only, depends_on)
NO → field disabled
YES → continue
3. Default action:
write to: run.input.data[field.fieldname]
4. Check schema._autosave:
if === 1 → debounce controller.autoSave(run)
if === 0 → wait for manual save buttonNo operation mapping. No action vocabulary. Just:
- Check editability
- Write to input.data
- Maybe auto-save
For MainGrid (Row Edit):
User types in grid cell
↓
Gatekeeper:
1. Is doc editable? (docstatus === 0)
NO → cell disabled
YES → continue
2. Is field editable? (field.read_only, field.in_list_view)
NO → cell disabled
YES → continue
3. Grid behavior (different from form):
Immediate save per row (not accumulation)
Create child run:
operation: 'update'
parent_run_id: grid_run.name
input: { name: row.name, [field]: value }Grid is different: immediate save, not accumulation.
🎯 The Two Patterns
| Container | Atomic Element | Edit Behavior | Save Behavior |
|---|---|---|---|
| MainForm | Schema field | Accumulate in run.input.data | Debounced auto-save (if _autosave=1) |
| MainGrid | Row (schema fields) | Immediate save per field | Immediate child run per change |
💡 Why It's Simple
Because schema defines everything:
Schema field properties already tell you:
├─ fieldtype → which component (FieldData, FieldCheck, etc.)
├─ read_only → is it editable
├─ read_only_depends_on → conditional editability
├─ allow_on_submit → edit after submission
├─ in_list_view → show in grid
└─ reqd → validation
Document state already tells you:
├─ docstatus → overall editability
├─ __islocal → is new document
└─ name → exists or not
Schema flags already tell you:
├─ is_submittable → workflow enabled
├─ _autosave → auto-save behavior
└─ istable → is child tableEverything you need to know is in schema + document state.
🚪 The Gatekeeper (Simple Check)
function canEditField(run, field) {
const doc = run.doc;
const schema = run.output.schema;
// Document level
if (schema.read_only) return false;
if (schema.is_submittable && doc.docstatus === 2) return false; // cancelled
if (schema.is_submittable && doc.docstatus === 1 && !field.allow_on_submit) return false; // submitted
// Field level
if (field.read_only) return false;
if (field.read_only_depends_on && !evaluateDependsOn(field.read_only_depends_on, doc)) return false;
return true;
}That's the gatekeeper. Simple boolean.
✅ What You're Saying
Stop creating:
- Complex action mappers
- Event buses
- Profile systems
- Interaction vocabularies
Just use:
- Schema (defines structure)
- Document state (defines editability)
- Simple gatekeeper (checks flags)
- Default behavior (accumulate or immediate save)
🎯 The Pattern Is:
1. Check if editable (gatekeeper)
2. If yes, apply default behavior for that container type
- MainForm → accumulate
- MainGrid → immediate save
3. DoneEvery atomic element is a schema field. Schema tells you what to do.
Is this the simplified, schema-centric approach you're advocating for?
The user is saying I'm still overcomplicating. Let me strip it down to the absolute basics:
UI field component = doc.field (one field from document) UI row = subset of document fields (multiple fields from document)
That's it. No gatekeepers, no complex checks, no patterns. Just:
- UI element corresponds to document field(s)
- User interacts with UI element
- What happens?
Let me focus ONLY on that simple relationship and the question of how UI interaction triggers new run() invocations.
✅ Stripped Down
📊 Simple Mapping
UIfield component = doc.field
UIrow = subset of doc.fieldsThat's it.
🤔 The Only Question
User edits UIfield → What happens?
Option 1: Write directly
onChange: run.input.data[field] = valueNo new run. Just write.
Option 2: Trigger new run
onChange: coworker.run({
operation: 'update',
parent_run_id: run.name,
input: { [field]: value }
})New run for each change.
Option 3: Hybrid
onChange:
write to run.input.data[field] = value
debounce → controller.autoSave(run)
→ creates child run🎯 Which one?
For MainForm: Option 1 or Option 3? For MainGrid: Option 1 or Option 2?
That's the only decision needed.
Ah! Now I get it. The user wants:
- UI component triggers new run() on EVERY action
- run() itself (via controller) decides if it's allowed
- No logic in components - components just invoke run()
- Config defines the wiring: MainForm.onFieldChange → run({operation: 'update'})
This is much cleaner:
- Components are truly dumb - they just call run() with fixed operation
- All business logic (gatekeeper, validation, etc.) inside run/controller
- Config is just: UI event → operation mapping
- Error handling happens in run(), returns to UI to display
So the pattern is:
Component event → Invoke run() with hardcoded operation
→ run() checks if allowed
→ If not allowed: return error
→ If allowed: execute
→ Result back to componentThis is actually elegant because:
- Components don't need any logic
- All rules centralized in controller
- Config is simple mapping
- Everything goes through same pipeline
- Errors handled consistently
Let me confirm this understanding.
💡 YES! That's Much Cleaner
🎯 The Pattern
Components Always Invoke run()
UIfield.onChange → new run({ operation: 'update', ... })
UIrow.onSelect → new run({ operation: 'takeone', ... })
SaveButton.onClick → new run({ operation: 'update', ... })
DeleteButton.onClick → new run({ operation: 'delete', ... })Every UI action = new run() call
🚪 Controller Decides Allowed/Blocked
run() receives request
↓
controller.execute()
↓
Check: Is this allowed?
- docstatus check
- read_only check
- field rules
- permissions
↓
If NOT allowed:
return { success: false, error: "Not editable" }
↓
UI shows error
If allowed:
execute operation
↓
return resultAll gatekeeping inside run/controller, not in components
📋 Config is Just Wiring
coworker._config.uiTriggers = {
'MainForm.onFieldChange': {
operation: 'update',
source_doctype: 'from_parent_run',
parent_run_id: 'from_parent_run'
},
'MainGrid.onRowSelect': {
operation: 'takeone',
source_doctype: 'from_grid_run'
},
'MainGrid.onCellEdit': {
operation: 'update',
source_doctype: 'from_grid_run'
},
'SaveButton.onClick': {
operation: 'update'
},
'DeleteButton.onClick': {
operation: 'delete'
}
}
```
**Simple mapping: UI event → operation type**
---
## ✅ **Benefits**
| Aspect | Benefit |
|--------|---------|
| **Components** | Zero logic, just invoke run() |
| **Controller** | Single place for all rules |
| **Errors** | Consistent handling |
| **Testing** | Test controller, not components |
| **Pipeline** | Every action goes through same flow |
---
## 🔄 **Example Flow**
```
1. User types in field in MainForm
↓
2. FieldData.onChange:
coworker.run({
operation: 'update', // ← From config
parent_run_id: run.name,
source_doctype: run.source_doctype,
input: { [field]: value }
})
↓
3. run() → controller.execute()
↓
4. Controller checks:
- doc.docstatus === 0?
- field.read_only?
- permissions?
↓
5a. If blocked:
return { success: false, error: "Document is submitted" }
↓
FieldData shows error
5b. If allowed:
execute update
↓
return { success: true, data: [...] }
↓
FieldData updates (or re-render)🎯 Key Insight
Pull controller logic OUT of UI by making every UI action invoke run(), then let controller be the gatekeeper.
Small delay for error feedback is acceptable trade-off for:
- Clean separation
- Centralized rules
- Consistent pipeline
- Testable logic
Is this the architecture you want?
The user wants to see how MainGrid currently works and understand how to apply the pattern of "always invoke new run()" from UI actions.
Looking at the MainGrid code:
const MainGrid = ({ run }) => {
// ... rendering table ...
// Each row is wrapped in RecordLink:
React.createElement(
RecordLink,
{
key: i,
record: row,
as: "tr",
className: CWStyles.grid.row,
},
// ... cells
)
}
// RecordLink component:
const RecordLink = ({ record, children, context = {}, as = "div", ...props }) => {
return React.createElement(
as,
{
...props,
onClick: () => coworker.onRecordClick(record, context),
style: { cursor: "pointer", ...props.style },
},
children
);
};So currently:
- Each row in MainGrid is a RecordLink
- When clicked, it calls
coworker.onRecordClick(record, context) - This is a handler method, not a direct run() call
The user wants to understand how this works now, and then presumably refactor it to follow the pattern of "UI action → new run()" directly.
Let me analyze what's happening:
- RecordLink is already triggering an action:
coworker.onRecordClick() - This is likely a handler that then calls run() internally
- The user wants to see if this should be simplified to directly call run()
I should explain what's currently happening and ask if we need to see the onRecordClick implementation to understand the full flow.
🔍 Current MainGrid Pattern
Looking at your code:
📊 The Flow Now
1. MainGrid Renders Rows
validData.map((row, i) =>
React.createElement(
RecordLink,
{
record: row,
as: "tr"
},
// cells...
)
)2. RecordLink Wraps Each Row
const RecordLink = ({ record, context, ...props }) => {
return React.createElement(
as,
{
onClick: () => coworker.onRecordClick(record, context),
// ↑ Calls handler method
},
children
);
};3. Handler Method (Not Shown)
coworker.onRecordClick = function(record, context) {
// ??? What happens here?
// Does it call run()?
// What operation?
}🤔 Questions About Current Implementation
- What does
coworker.onRecordClick()do?- Does it invoke
run({ operation: 'takeone', ... })? - Or some other logic?
- Does it invoke
- What's the context parameter?
- Where does it come from?
- What's in it?
- Where is the parent run context?
- RecordLink doesn't receive the grid's
run - How does it know source_doctype?
- How does it set parent_run_id?
- RecordLink doesn't receive the grid's
🎯 What It SHOULD Be (Based on Your Pattern)
Direct run() Invocation:
const RecordLink = ({ record, parentRun, ...props }) => {
return React.createElement(
as,
{
onClick: () => {
coworker.run({
operation: 'takeone', // ← From config: MainGrid.onRowClick
source_doctype: parentRun.source_doctype,
query: { where: { name: record.name }},
parent_run_id: parentRun.name,
options: { render: true }
});
}
},
children
);
};Pass parentRun from MainGrid:
const MainGrid = ({ run }) => {
// ...
validData.map((row, i) =>
React.createElement(
RecordLink,
{
record: row,
parentRun: run, // ← Pass context
as: "tr"
},
// cells...
)
)
}🎯 Key Difference
| Current | Proposed |
|---|---|
onClick: () => coworker.onRecordClick(record, context) | onClick: () => coworker.run({ operation: 'takeone', ... }) |
| Handler method decides what to do | Direct run() invocation |
| Context passed separately | Context extracted from parentRun |
❓ Need to See
Can you show:
- What's in
coworker.onRecordClick()? - What context is passed to RecordLink?
- How does current implementation know which doctype/operation to use?
Then we can redesign it to follow the "UI action → direct run()" pattern.
Good! Now I can see the current implementation. Let me analyze:
Current Pattern:
RecordLink.onClick → coworker.onRecordClick(record, context) → coworker.run({
operation: 'takeone',
doctype: record.doctype,
query: { where: { name: record.name } },
options: { render: true }
})Issues:
- No parent_run_id passed (no context chain)
- No flow_id (editing session not tracked)
- doctype comes from record, not from grid's run context
- Context parameter is vague/optional
What the user wants: Direct run() invocation from UI components, with config defining the mapping.
Better Pattern:
RecordLink.onClick → coworker.run({
operation: 'takeone', // from config: MainGrid.onRowClick
source_doctype: parentRun.source_doctype, // from parent context
query: { where: { name: record.name }},
parent_run_id: parentRun.name, // context chain
options: { render: true }
})This is simpler and more explicit - no intermediate handler method needed.
✅ I See It Now
📊 Current Pattern
RecordLink.onClick
↓
coworker.onRecordClick(record, context)
↓
coworker.run({
operation: 'takeone',
doctype: record.doctype,
query: { where: { name: record.name }},
options: { render: true }
})Handler method as intermediary
❌ Issues With Current
1. No Parent Context
// Missing:
parent_run_id: ???
flow_id: ???New run doesn't know it came from grid.
2. Doctype from Record
doctype: record.doctypeAssumes record has doctype field. What if it doesn't?
3. Vague Context Parameter
context = {}
...context // What goes here?Unclear what context means or contains.
✅ Simplified Pattern (What You Want)
Remove Handler Method
Just call run() directly:
const RecordLink = ({ record, parentRun, ...props }) => {
return React.createElement(
as,
{
onClick: () => {
coworker.run({
operation: 'takeone',
source_doctype: parentRun.source_doctype,
query: { where: { name: record.name }},
parent_run_id: parentRun.name,
options: { render: true }
});
}
},
children
);
};MainGrid Passes Context
const MainGrid = ({ run }) => {
// ...
validData.map((row, i) =>
React.createElement(
RecordLink,
{
record: row,
parentRun: run, // ← Grid's run
as: "tr"
},
// cells...
)
)
}🎯 Benefits
| Aspect | Current | Simplified |
|---|---|---|
| Indirection | Component → handler → run | Component → run (direct) |
| Context chain | Lost | Preserved (parent_run_id) |
| Doctype source | From record (fragile) | From parent run (reliable) |
| Clarity | What's in context? | Explicit parameters |
📋 Optional: Config-Driven
If you want operation from config:
coworker._config.uiTriggers = {
'MainGrid.onRowClick': {
operation: 'takeone'
}
}
// In RecordLink:
const trigger = coworker._config.uiTriggers['MainGrid.onRowClick'];
coworker.run({
operation: trigger.operation, // ← From config
source_doctype: parentRun.source_doctype,
query: { where: { name: record.name }},
parent_run_id: parentRun.name,
options: { render: true }
});❓ Decision
Do you want to:
A) Direct invocation (no config)
- Hardcode
operation: 'takeone'in RecordLink - Simple, explicit
B) Config-driven (from config)
- Look up operation from config
- More flexible, but adds lookup
Which matches your architecture better?
The user is asking if they can have the UI trigger configuration in the config for MainGrid. Looking at the context, they want to define the behavior (like onRowClick → operation: 'takeone') in the config rather than hardcoding it in the component.
Based on their existing config structure that has:
- operations config
- views config
- field handlers config
They're asking if they can add something like:
coworker._config.uiTriggers = {
'MainGrid.onRowClick': {
operation: 'takeone'
}
}The answer is YES, absolutely. This fits perfectly with their config-driven, schema-first approach. The component would then look up this config to know what operation to invoke.
Let me confirm this is possible and show how it would work.
✅ YES - Absolutely
📋 Config Structure
coworker._config = {
// Existing configs...
operations: { ... },
views: { ... },
// NEW: UI Triggers
uiTriggers: {
'MainGrid.onRowClick': {
operation: 'takeone',
options: {
render: true
}
},
'MainGrid.onCellEdit': {
operation: 'update',
options: {
render: false // Don't re-render on each cell edit
}
},
'MainForm.onFieldChange': {
operation: 'update',
options: {
render: false,
debounce: 300
}
},
'MainForm.onSave': {
operation: 'update',
options: {
render: true
}
}
}
}🔧 RecordLink Uses Config
const RecordLink = ({ record, parentRun, triggerKey, ...props }) => {
const trigger = coworker._config.uiTriggers[triggerKey];
if (!trigger) {
console.error(`No trigger config for: ${triggerKey}`);
return null;
}
return React.createElement(
as,
{
onClick: () => {
coworker.run({
operation: trigger.operation, // ← From config
source_doctype: parentRun.source_doctype,
query: { where: { name: record.name }},
parent_run_id: parentRun.name,
options: trigger.options // ← From config
});
}
},
children
);
};🔧 MainGrid Passes Trigger Key
const MainGrid = ({ run }) => {
// ...
validData.map((row, i) =>
React.createElement(
RecordLink,
{
record: row,
parentRun: run,
triggerKey: 'MainGrid.onRowClick', // ← Config key
as: "tr"
},
// cells...
)
)
}✅ Benefits
| Aspect | Value |
|---|---|
| Centralized | All UI behaviors in one config |
| Flexible | Change operation without touching components |
| Consistent | Same pattern for MainGrid, MainForm, etc. |
| Discoverable | See all UI triggers in one place |
🎯 Yes, This Fits Your Architecture Perfectly
Config-driven, schema-first approach where components are dumb and config defines behavior.
The user is asking if we can push even MORE to config. Let me think about what's still hardcoded in the components vs what could be in config.
Current pattern:
const RecordLink = ({ record, parentRun, triggerKey, ...props }) => {
const trigger = coworker._config.uiTriggers[triggerKey];
return React.createElement(
as,
{
onClick: () => {
coworker.run({
operation: trigger.operation,
source_doctype: parentRun.source_doctype,
query: { where: { name: record.name }},
parent_run_id: parentRun.name,
options: trigger.options
});
}
},
children
);
};What's still hardcoded here:
source_doctype: parentRun.source_doctype- extracting from parentquery: { where: { name: record.name }}- building query structureparent_run_id: parentRun.name- extracting parent ID- The logic of how to build the run() parameters
Could we push the PARAMETER BUILDING LOGIC to config?
Instead of:
uiTriggers: {
'MainGrid.onRowClick': {
operation: 'takeone',
options: { render: true }
}
}We could have:
uiTriggers: {
'MainGrid.onRowClick': {
operation: 'takeone',
buildParams: (record, parentRun) => ({
operation: 'takeone',
source_doctype: parentRun.source_doctype,
query: { where: { name: record.name }},
parent_run_id: parentRun.name,
options: { render: true }
})
}
}Or even more generic - have a TEMPLATE system:
uiTriggers: {
'MainGrid.onRowClick': {
operation: 'takeone',
source_doctype: '${parentRun.source_doctype}',
query: { where: { name: '${record.name}' }},
parent_run_id: '${parentRun.name}',
options: { render: true }
}
}Then RecordLink becomes COMPLETELY generic - just reads config and interpolates.
Or we could have a function-based config where you pass a function that builds the params.
Let me suggest the most minimal component code possible.
🤔 Can We Push More to Config?
📊 What's Still Hardcoded in Component
// RecordLink still has:
1. Parameter extraction logic: parentRun.source_doctype
2. Query building logic: { where: { name: record.name }}
3. Parent reference logic: parent_run_id: parentRun.name
4. The structure of run() call itself💡 Option: Function in Config
uiTriggers: {
'MainGrid.onRowClick': {
buildRunParams: (record, parentRun) => ({
operation: 'takeone',
source_doctype: parentRun.source_doctype,
query: { where: { name: record.name }},
parent_run_id: parentRun.name,
options: { render: true }
})
}
}Component Becomes:
const RecordLink = ({ record, parentRun, triggerKey, ...props }) => {
const trigger = coworker._config.uiTriggers[triggerKey];
return React.createElement(as, {
onClick: () => coworker.run(
trigger.buildRunParams(record, parentRun)
)
}, children);
};Even simpler component!
💡 Option: Template Strings
uiTriggers: {
'MainGrid.onRowClick': {
operation: 'takeone',
source_doctype: '${parentRun.source_doctype}',
query: { where: { name: '${record.name}' }},
parent_run_id: '${parentRun.name}',
options: { render: true }
}
}Component Becomes:
const RecordLink = ({ record, parentRun, triggerKey, ...props }) => {
const trigger = coworker._config.uiTriggers[triggerKey];
const params = coworker._interpolate(trigger, { record, parentRun });
return React.createElement(as, {
onClick: () => coworker.run(params)
}, children);
};💡 Option: Generic UI Action Handler
// Ultra-minimal component:
const RecordLink = ({ record, parentRun, triggerKey, ...props }) => {
return React.createElement(as, {
onClick: () => coworker.handleUITrigger(triggerKey, { record, parentRun })
}, children);
};
// Generic handler:
coworker.handleUITrigger = function(triggerKey, context) {
const trigger = this._config.uiTriggers[triggerKey];
const params = trigger.buildRunParams(context);
return this.run(params);
};Component is now COMPLETELY generic - just passes trigger key + context
🎯 Absolute Minimal Component
const RecordLink = ({ record, parentRun, triggerKey, as, ...props }) => {
return React.createElement(as, {
...props,
onClick: () => coworker.handleUITrigger(triggerKey, { record, parentRun })
}, children);
};That's it. 4 lines. Zero business logic.
✅ Recommendation
Use function in config:
Config:
uiTriggers: {
'MainGrid.onRowClick': {
buildRunParams: (ctx) => ({
operation: 'takeone',
source_doctype: ctx.parentRun.source_doctype,
query: { where: { name: ctx.record.name }},
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
},
'MainGrid.onCellEdit': {
buildRunParams: (ctx) => ({
operation: 'update',
source_doctype: ctx.parentRun.source_doctype,
input: { [ctx.field]: ctx.value },
query: { where: { name: ctx.record.name }},
parent_run_id: ctx.parentRun.name,
options: { render: false }
})
}
}Generic Handler:
coworker.handleUITrigger = function(triggerKey, context) {
const trigger = this._config.uiTriggers[triggerKey];
if (!trigger) throw new Error(`Unknown trigger: ${triggerKey}`);
const params = trigger.buildRunParams(context);
return this.run(params);
};Component:
const RecordLink = ({ record, parentRun, triggerKey, as, ...props }) => {
return React.createElement(as, {
...props,
onClick: () => coworker.handleUITrigger(triggerKey, { record, parentRun })
}, children);
};🎯 This is Minimal
- Component: 4 lines, zero logic
- Handler: 4 lines, generic
- Config: All business rules
Can't get much simpler than this!
The user wants to see the complete MainGrid code with the new minimal approach where:
- RecordLink uses coworker.handleUITrigger
- Everything is driven by config
- Component is minimal with no business logic
Let me write out the full MainGrid component with the changes applied.
📄 Final MainGrid Code
/**
* RecordLink - Generic clickable wrapper (MINIMAL)
*/
const RecordLink = ({ record, parentRun, triggerKey, as = "div", children, ...props }) => {
return React.createElement(
as,
{
...props,
onClick: () => coworker.handleUITrigger(triggerKey, { record, parentRun }),
style: { cursor: "pointer", ...props.style }
},
children
);
};
/**
* MainGrid - List view with table
*/
const MainGrid = ({ run }) => {
const data = run.output?.data;
// Filter out null/undefined records
const validData = data?.filter(row => row != null) || [];
if (validData.length === 0) {
return React.createElement(
"div",
{ className: CWStyles.alert.info },
"No records found"
);
}
// Get keys from first valid record
const keys = Object.keys(validData[0] || {});
return React.createElement(
"div",
{ className: CWStyles.grid.wrapper },
// Header
React.createElement(
"div",
{ className: CWStyles.grid.header },
React.createElement("h2", {}, run.source_doctype || "List"),
React.createElement(
"div",
{ className: CWStyles.grid.toolbar },
React.createElement(
"button",
{ className: CWStyles.button.primary },
"New"
)
)
),
// Body
React.createElement(
"div",
{ className: CWStyles.grid.body },
React.createElement(
"table",
{ className: CWStyles.table.base + " " + CWStyles.table.striped },
// Header row
React.createElement(
"thead",
{},
React.createElement(
"tr",
{ className: CWStyles.grid.row },
keys.map((key) =>
React.createElement(
"th",
{ key: key, className: CWStyles.grid.cell },
key
)
)
)
),
// Data rows
React.createElement(
"tbody",
{},
validData.map((row, i) =>
React.createElement(
RecordLink,
{
key: i,
record: row,
parentRun: run,
triggerKey: 'MainGrid.onRowClick', // ← Config-driven
as: "tr",
className: CWStyles.grid.row
},
keys.map((key) =>
React.createElement(
"td",
{ key: key, className: CWStyles.grid.cell },
String(row?.[key] ?? "")
)
)
)
)
)
)
)
);
};
// Register
window.MainGrid = MainGrid;
window.RecordLink = RecordLink;🎯 Key Changes
| Before | After |
|---|---|
onClick: () => coworker.onRecordClick(record, context) | onClick: () => coworker.handleUITrigger(triggerKey, { record, parentRun }) |
RecordLink receives vague context | RecordLink receives explicit parentRun |
| No trigger key | triggerKey: 'MainGrid.onRowClick' |
| Handler method with hardcoded logic | Generic handler + config |
📋 Required Config
coworker._config.uiTriggers = {
'MainGrid.onRowClick': {
buildRunParams: (ctx) => ({
operation: 'takeone',
source_doctype: ctx.parentRun.source_doctype,
query: { where: { name: ctx.record.name }},
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
}
};🔧 Required Handler
coworker.handleUITrigger = function(triggerKey, context) {
const trigger = this._config.uiTriggers[triggerKey];
if (!trigger) {
throw new Error(`Unknown UI trigger: ${triggerKey}`);
}
const params = trigger.buildRunParams(context);
return this.run(params);
};✅ Result
- RecordLink: 9 lines, zero business logic
- MainGrid: Same structure, just passes
triggerKeyandparentRun - All behavior: Defined in config
- Extensible: Add new triggers without touching components
Clean, minimal, config-driven. 🎯
The user wants to use TanStack Table (React Table) in UMD format. Let me think about how this changes the architecture.
TanStack Table is a headless table library - it provides the logic and state management but the user renders the UI themselves.
Key changes:
- MainGrid would use TanStack Table's hooks/functions for table state management
- Still need to maintain the config-driven trigger pattern
- RecordLink still wraps rows with onClick
- TanStack handles sorting, filtering, pagination, column management
- The rendering is still custom but driven by TanStack's column definitions and data
The user is asking how the architecture changes, so I should focus on:
- How to integrate TanStack Table while keeping the config-driven pattern
- Where RecordLink fits in
- How triggers work with TanStack's row model
- Column configuration
- Whether the trigger pattern changes
Key insight: TanStack Table provides the table instance and helpers, but the rendering is still custom. So the trigger pattern doesn't fundamentally change - we're still rendering rows and cells, just with TanStack managing the data/state.
Main changes:
- Add TanStack Table UMD script
- Define columns (can be in config)
- Use useReactTable hook to create table instance
- Render using table instance methods
- RecordLink still wraps rows with same trigger pattern
The config-driven trigger approach actually works perfectly with TanStack because TanStack is headless - we still control the rendering.
🔄 TanStack Table Integration
📊 What Changes
Before (Native Table):
- Manual column rendering from
Object.keys() - Manual row iteration
- No sorting/filtering/pagination logic
After (TanStack Table):
- Column definitions (can be in config)
- TanStack manages table state
- TanStack provides sorted/filtered/paginated data
- RecordLink pattern stays the same
📋 Updated MainGrid with TanStack
/**
* MainGrid - List view with TanStack Table
*/
const MainGrid = ({ run }) => {
const data = run.output?.data;
const validData = data?.filter(row => row != null) || [];
if (validData.length === 0) {
return React.createElement(
"div",
{ className: CWStyles.alert.info },
"No records found"
);
}
// ✅ Column definitions (can move to config)
const columns = React.useMemo(() => {
const firstRow = validData[0] || {};
const keys = Object.keys(firstRow);
return keys.map(key => ({
accessorKey: key,
header: key,
cell: info => String(info.getValue() ?? "")
}));
}, [validData]);
// ✅ Create table instance
const table = ReactTable.useReactTable({
data: validData,
columns: columns,
getCoreRowModel: ReactTable.getCoreRowModel(),
getSortedRowModel: ReactTable.getSortedRowModel(),
getFilteredRowModel: ReactTable.getFilteredRowModel(),
getPaginationRowModel: ReactTable.getPaginationRowModel()
});
return React.createElement(
"div",
{ className: CWStyles.grid.wrapper },
// Header
React.createElement(
"div",
{ className: CWStyles.grid.header },
React.createElement("h2", {}, run.source_doctype || "List"),
React.createElement(
"div",
{ className: CWStyles.grid.toolbar },
React.createElement(
"button",
{ className: CWStyles.button.primary },
"New"
)
)
),
// Table
React.createElement(
"div",
{ className: CWStyles.grid.body },
React.createElement(
"table",
{ className: CWStyles.table.base + " " + CWStyles.table.striped },
// Header
React.createElement(
"thead",
{},
table.getHeaderGroups().map(headerGroup =>
React.createElement(
"tr",
{ key: headerGroup.id, className: CWStyles.grid.row },
headerGroup.headers.map(header =>
React.createElement(
"th",
{
key: header.id,
className: CWStyles.grid.cell,
onClick: header.column.getToggleSortingHandler(),
style: { cursor: "pointer" }
},
ReactTable.flexRender(
header.column.columnDef.header,
header.getContext()
),
// Sort indicator
{ asc: ' 🔼', desc: ' 🔽' }[header.column.getIsSorted()] ?? null
)
)
)
)
),
// Body with RecordLink (PATTERN UNCHANGED)
React.createElement(
"tbody",
{},
table.getRowModel().rows.map(row =>
React.createElement(
RecordLink,
{
key: row.id,
record: row.original, // ← TanStack provides original row data
parentRun: run,
triggerKey: 'MainGrid.onRowClick', // ← Same config-driven pattern
as: "tr",
className: CWStyles.grid.row
},
row.getVisibleCells().map(cell =>
React.createElement(
"td",
{ key: cell.id, className: CWStyles.grid.cell },
ReactTable.flexRender(
cell.column.columnDef.cell,
cell.getContext()
)
)
)
)
)
)
)
),
// Pagination controls (optional)
React.createElement(
"div",
{ className: CWStyles.grid.pagination },
React.createElement(
"button",
{
onClick: () => table.previousPage(),
disabled: !table.getCanPreviousPage()
},
"Previous"
),
React.createElement("span", {},
`Page ${table.getState().pagination.pageIndex + 1} of ${table.getPageCount()}`
),
React.createElement(
"button",
{
onClick: () => table.nextPage(),
disabled: !table.getCanNextPage()
},
"Next"
)
)
);
};📋 Optional: Column Config
Move column definitions to config:
coworker._config.gridColumns = {
'Customer': [
{
accessorKey: 'name',
header: 'Customer ID',
cell: info => info.getValue()
},
{
accessorKey: 'customer_name',
header: 'Name',
cell: info => info.getValue()
},
{
accessorKey: 'customer_group',
header: 'Group',
cell: info => info.getValue()
}
]
// ... other doctypes
};
// In MainGrid:
const columns = React.useMemo(() => {
const configColumns = coworker._config.gridColumns?.[run.source_doctype];
if (configColumns) return configColumns;
// Fallback: auto-generate from data
const firstRow = validData[0] || {};
return Object.keys(firstRow).map(key => ({
accessorKey: key,
header: key,
cell: info => String(info.getValue() ?? "")
}));
}, [run.source_doctype, validData]);🔧 Required: Load TanStack Table UMD
<script src="https://unpkg.com/@tanstack/react-table@8/build/umd/index.production.js"></script>Access as window.ReactTable
🎯 What Stays the Same
| Aspect | Unchanged |
|---|---|
| RecordLink pattern | Still wraps rows with triggerKey |
| Config-driven triggers | MainGrid.onRowClick still in config |
| run() invocation | Still through handleUITrigger |
| Parent context | Still passes parentRun |
✅ What Changes
| Aspect | Change |
|---|---|
| Column rendering | TanStack manages columns |
| Data access | row.original instead of row |
| Sorting | Built-in with TanStack |
| Filtering | Built-in with TanStack |
| Pagination | Built-in with TanStack |
| Cell rendering | ReactTable.flexRender() |
🎯 Key Insight
TanStack Table is headless - it provides state management but you still control rendering.
Your trigger pattern fits perfectly because you're still rendering <tr> elements - just with TanStack's data and helpers.
The architecture doesn't fundamentally change. You just get better table features (sorting, filtering, pagination) while keeping the same config-driven trigger system.
Ah, excellent point! The user is reminding me that:
- The view is determined by run() through the resolution process
- Columns should come from SCHEMA fields where
in_list_view: true - No hardcoding - everything driven by schema
So the MainGrid should:
- Get schema from run.output.schema
- Filter fields where field.in_list_view === true
- Use those fields to define columns for TanStack Table
- Schema-first approach, not data-first
Let me redesign this properly.
✅ Correct - Schema-Driven, Not Data-Driven
🎯 The Right Flow
run() called
↓
_resolveAll()
↓
operation → view (from config)
↓
view → component (MainGrid)
↓
controller.execute()
↓
requiresSchema: true → loads schema
↓
run.output = { data: [...], schema: {...} }
↓
MainGrid receives run with schema
↓
Read schema.fields where in_list_view: true
↓
Create columns from schema fields (not from data keys)📋 Corrected MainGrid with TanStack
/**
* MainGrid - Schema-driven list view with TanStack Table
*/
const MainGrid = ({ run }) => {
const data = run.output?.data;
const schema = run.output?.schema;
// Validate
const validData = data?.filter(row => row != null) || [];
if (!schema) {
return React.createElement(
"div",
{ className: CWStyles.alert.warning },
"Loading schema..."
);
}
if (validData.length === 0) {
return React.createElement(
"div",
{ className: CWStyles.alert.info },
"No records found"
);
}
// ✅ SCHEMA-DRIVEN: Get columns from schema.fields where in_list_view
const columns = React.useMemo(() => {
const listFields = schema.fields.filter(f => f.in_list_view);
return listFields.map(field => ({
accessorKey: field.fieldname,
header: field.label || field.fieldname,
cell: info => {
const value = info.getValue();
// Field type specific rendering
if (field.fieldtype === 'Check') {
return value ? '✓' : '';
}
if (field.fieldtype === 'Currency') {
return value ? `$${Number(value).toFixed(2)}` : '';
}
return String(value ?? "");
}
}));
}, [schema]);
// ✅ Create table instance
const table = ReactTable.useReactTable({
data: validData,
columns: columns,
getCoreRowModel: ReactTable.getCoreRowModel(),
getSortedRowModel: ReactTable.getSortedRowModel(),
getFilteredRowModel: ReactTable.getFilteredRowModel(),
getPaginationRowModel: ReactTable.getPaginationRowModel()
});
return React.createElement(
"div",
{ className: CWStyles.grid.wrapper },
// Header
React.createElement(
"div",
{ className: CWStyles.grid.header },
React.createElement("h2", {}, schema._schema_doctype || "List"),
React.createElement(
"div",
{ className: CWStyles.grid.toolbar },
React.createElement(
"button",
{ className: CWStyles.button.primary },
"New"
)
)
),
// Table
React.createElement(
"div",
{ className: CWStyles.grid.body },
React.createElement(
"table",
{ className: CWStyles.table.base + " " + CWStyles.table.striped },
// Header
React.createElement(
"thead",
{},
table.getHeaderGroups().map(headerGroup =>
React.createElement(
"tr",
{ key: headerGroup.id, className: CWStyles.grid.row },
headerGroup.headers.map(header =>
React.createElement(
"th",
{
key: header.id,
className: CWStyles.grid.cell,
onClick: header.column.getToggleSortingHandler(),
style: { cursor: "pointer" }
},
ReactTable.flexRender(
header.column.columnDef.header,
header.getContext()
),
{ asc: ' 🔼', desc: ' 🔽' }[header.column.getIsSorted()] ?? null
)
)
)
)
),
// Body - RecordLink wraps rows
React.createElement(
"tbody",
{},
table.getRowModel().rows.map(row =>
React.createElement(
RecordLink,
{
key: row.id,
record: row.original,
parentRun: run,
triggerKey: 'MainGrid.onRowClick',
as: "tr",
className: CWStyles.grid.row
},
row.getVisibleCells().map(cell =>
React.createElement(
"td",
{ key: cell.id, className: CWStyles.grid.cell },
ReactTable.flexRender(
cell.column.columnDef.cell,
cell.getContext()
)
)
)
)
)
)
)
),
// Pagination
React.createElement(
"div",
{ className: CWStyles.grid.pagination },
React.createElement(
"button",
{
onClick: () => table.previousPage(),
disabled: !table.getCanPreviousPage(),
className: CWStyles.button.secondary
},
"Previous"
),
React.createElement("span", {},
`Page ${table.getState().pagination.pageIndex + 1} of ${table.getPageCount()}`
),
React.createElement(
"button",
{
onClick: () => table.nextPage(),
disabled: !table.getCanNextPage(),
className: CWStyles.button.secondary
},
"Next"
)
)
);
};🎯 Key Differences
| Aspect | Wrong (Before) | Correct (Now) |
|---|---|---|
| Column source | Object.keys(data[0]) | schema.fields.filter(f => f.in_list_view) |
| Column header | key name | field.label |
| Column order | Random (object key order) | Schema field_order |
| Title | run.source_doctype | schema._schema_doctype |
| Cell rendering | Generic string | Field type aware |
📋 Schema Example
{
"_schema_doctype": "Customer",
"fields": [
{
"fieldname": "name",
"fieldtype": "Data",
"label": "Customer ID",
"in_list_view": 1 // ← Show in grid
},
{
"fieldname": "customer_name",
"fieldtype": "Data",
"label": "Name",
"in_list_view": 1 // ← Show in grid
},
{
"fieldname": "email",
"fieldtype": "Data",
"label": "Email",
"in_list_view": 0 // ← Hide in grid
},
{
"fieldname": "is_active",
"fieldtype": "Check",
"label": "Active",
"in_list_view": 1 // ← Show in grid
}
]
}Result: Grid shows only name, customer_name, is_active columns
✅ Now Fully Schema-Driven
- Columns from
schema.fields - Filtered by
in_list_view - Labels from
field.label - Cell rendering aware of
field.fieldtype - No hardcoding
- No data-driven column inference
Perfect alignment with your schema-first architecture! 🎯
The user is asking if they just need to add:
- The
coworker.handleUITriggerfunction - The MainGrid code I provided
Let me confirm what they need:
- coworker.handleUITrigger - Yes, this is the generic handler that looks up config and calls run()
- The MainGrid code - Yes, the schema-driven version with TanStack Table
- RecordLink - Yes, the minimal version that calls handleUITrigger
- Config - They need the uiTriggers config with buildRunParams
- Remove old code - They should remove the old
coworker.onRecordClickhandler method
So the answer is: Yes, but they need:
- handleUITrigger function
- Updated MainGrid (schema-driven with TanStack)
- Updated RecordLink (minimal)
- Config with uiTriggers
- Remove old onRecordClick handler
✅ Yes - Exactly
📋 What You Need
1. Generic Handler (Add Once)
coworker.handleUITrigger = function(triggerKey, context) {
const trigger = this._config.uiTriggers[triggerKey];
if (!trigger) {
throw new Error(`Unknown UI trigger: ${triggerKey}`);
}
const params = trigger.buildRunParams(context);
return this.run(params);
};2. Config (Add Once)
coworker._config.uiTriggers = {
'MainGrid.onRowClick': {
buildRunParams: (ctx) => ({
operation: 'takeone',
source_doctype: ctx.parentRun.source_doctype,
query: { where: { name: ctx.record.name }},
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
}
};3. Updated Components
- RecordLink (minimal version I provided)
- MainGrid (schema-driven with TanStack version I provided)
4. Remove Old Code
// DELETE THIS:
coworker.onRecordClick = function(record, context = {}) {
return this.run({
operation: 'takeone',
doctype: record.doctype,
query: { where: { name: record.name } },
options: { render: true },
...context
});
};✅ That's It
- Add
handleUITriggerfunction - Add config with
uiTriggers - Use my MainGrid code (schema-driven)
- Remove old
onRecordClickhandler
Done! 🎯
The user wants me to summarize the UI actions as config proposal before implementing. Let me create a clear, comprehensive summary of the config structure for UI triggers.
I should cover:
- The overall structure
- All the different UI triggers we discussed
- The pattern for each container (MainForm, MainGrid, etc.)
- How context flows
- What operations map to what actions
Let me organize this as a clear config proposal.
📋 UI Actions Config Proposal - Summary
🎯 Core Concept
Every UI action → config-defined trigger → run() invocation
User interacts with UI element
↓
Component calls: coworker.handleUITrigger(triggerKey, context)
↓
Config defines: operation + parameters
↓
Handler builds params and calls: coworker.run(params)📊 Config Structure
coworker._config.uiTriggers = {
// ====================================
// MAINGRID TRIGGERS
// ====================================
'MainGrid.onRowClick': {
buildRunParams: (ctx) => ({
operation: 'takeone',
source_doctype: ctx.parentRun.source_doctype,
query: { where: { name: ctx.record.name }},
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
},
'MainGrid.onCellEdit': {
buildRunParams: (ctx) => ({
operation: 'update',
source_doctype: ctx.parentRun.source_doctype,
input: { [ctx.field.fieldname]: ctx.value },
query: { where: { name: ctx.record.name }},
parent_run_id: ctx.parentRun.name,
options: { render: false }
})
},
'MainGrid.onNewButton': {
buildRunParams: (ctx) => ({
operation: 'create',
target_doctype: ctx.parentRun.source_doctype,
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
},
// ====================================
// MAINFORM TRIGGERS
// ====================================
'MainForm.onFieldChange': {
buildRunParams: (ctx) => ({
operation: 'update',
source_doctype: ctx.parentRun.source_doctype,
input: { [ctx.field.fieldname]: ctx.value },
query: { where: { name: ctx.parentRun.doc.name }},
parent_run_id: ctx.parentRun.name,
options: {
render: false,
draft: true
}
})
},
'MainForm.onSaveButton': {
buildRunParams: (ctx) => ({
operation: 'update',
source_doctype: ctx.parentRun.source_doctype,
input: ctx.parentRun.input.data, // All accumulated changes
query: { where: { name: ctx.parentRun.doc.name }},
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
},
'MainForm.onDeleteButton': {
buildRunParams: (ctx) => ({
operation: 'delete',
source_doctype: ctx.parentRun.source_doctype,
query: { where: { name: ctx.parentRun.doc.name }},
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
},
'MainForm.onSubmitButton': {
buildRunParams: (ctx) => ({
operation: 'submit',
source_doctype: ctx.parentRun.source_doctype,
query: { where: { name: ctx.parentRun.doc.name }},
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
},
'MainForm.onCancelButton': {
buildRunParams: (ctx) => ({
operation: 'cancel',
source_doctype: ctx.parentRun.source_doctype,
query: { where: { name: ctx.parentRun.doc.name }},
parent_run_id: ctx.parentRun.name,
options: { render: true }
})
},
// ====================================
// MAINCHAT TRIGGERS
// ====================================
'MainChat.onSendMessage': {
buildRunParams: (ctx) => ({
operation: 'ai_chat',
input: { message: ctx.message },
parent_run_id: ctx.parentRun.name,
options: { render: false }
})
}
}🔧 Generic Handler (Add Once)
coworker.handleUITrigger = function(triggerKey, context) {
const trigger = this._config.uiTriggers[triggerKey];
if (!trigger) {
throw new Error(`Unknown UI trigger: ${triggerKey}`);
}
const params = trigger.buildRunParams(context);
return this.run(params);
};📊 Context Objects by Container
MainGrid Context
{
record: { name: 'CUST-001', ... }, // Row data
parentRun: run, // Grid's run
field: { fieldname: '...' }, // For cell edit
value: 'new value' // For cell edit
}MainForm Context
{
parentRun: run, // Form's run
field: { fieldname: '...' }, // For field change
value: 'new value' // For field change
}MainChat Context
{
parentRun: run, // Chat's run
message: 'user message text'
}🎯 Component Changes
Components Become Minimal
// Instead of:
onClick: () => coworker.onRecordClick(record, context)
// Now:
onClick: () => coworker.handleUITrigger('MainGrid.onRowClick', { record, parentRun })No Business Logic in Components
- RecordLink: 4 lines
- Field components: Just call handleUITrigger
- All logic in config
✅ Benefits
| Aspect | Value |
|---|---|
| Centralized | All UI behaviors in one config file |
| Discoverable | See all triggers at a glance |
| Testable | Test config independently of UI |
| Flexible | Change operations without touching components |
| Consistent | Same pattern everywhere |
| Parent context | Always preserved via parent_run_id |
🚀 What Gets Removed
// DELETE ALL THESE:
coworker.onRecordClick = function(record, context) { ... }
// Any other onClick/onChange handler methods
// Business logic in components🎯 Summary
Pattern:
- Component fires trigger with context
- Config maps trigger to operation
- Handler builds params
- run() executes
Result:
- Zero business logic in UI components
- All behavior defined in config
- Consistent run() invocation pattern
- Full parent context chain preserved
This is the proposal - approve? ✅
The user is asking to step back and reconsider the approach. They're recognizing that what we're designing is essentially a state machine with transitions based on:
- Component state (which component is rendered)
- Document state (docstatus, data, etc.)
- Schema (global flags like is_submittable)
- View (form, grid, chat)
They want to see simple examples from other OSS projects that handle this kind of state machine / transition logic in a simple way.
This is a good question - let me think about what they're really asking:
- They have a CoworkerState already
- They want to add "next transitions" - what operations are valid from current state
- The transitions depend on: component, document, schema, view
- They want simple OSS examples of this pattern
Good examples might be:
- XState (state machines for UIs)
- Redux state machines
- Frappe's DocType state transitions (they're building on Frappe patterns)
- Form validation state machines
- Workflow engines (simple ones)
But they want "very simple examples" - so not complex frameworks, but simple patterns that OSS projects use for this.
Let me think about what simple patterns exist:
- Simple state object with allowed_transitions array
- Finite state machine with transition rules
- Action/reducer pattern with allowed actions
- Document status workflow (like Frappe's docstatus)
I should provide concrete, simple examples from real projects that handle similar problems.
🤔 Good Insight - It IS a State Machine
You're right. What we're building is:
Current State (component + document + schema + view)
↓
Possible Transitions (what UI actions are allowed?)
↓
Next State (new run())💡 Simple OSS Examples
1. Frappe's Document State Machine (Your Inspiration)
# Frappe's workflow transitions
{
"docstatus": 0, # Draft
"allowed_actions": ["save", "submit", "delete"]
}
{
"docstatus": 1, # Submitted
"allowed_actions": ["cancel", "amend"]
}
{
"docstatus": 2, # Cancelled
"allowed_actions": ["amend"]
}Super simple: Current docstatus → allowed actions
2. GitHub Issues State Machine
{
"state": "open",
"transitions": ["close", "lock", "pin", "edit"]
}
{
"state": "closed",
"transitions": ["reopen", "lock", "edit"]
}Pattern: state → allowed transitions list
3. Stripe Payment States
const PAYMENT_TRANSITIONS = {
'draft': ['authorize', 'cancel'],
'authorized': ['capture', 'void'],
'captured': ['refund'],
'refunded': [],
'voided': []
}
function getAllowedActions(currentState) {
return PAYMENT_TRANSITIONS[currentState] || [];
}Pattern: Simple object lookup
4. React Hook Form State
const formState = {
isDirty: false,
isValid: false,
isSubmitting: false
}
// Allowed actions depend on state
if (!formState.isValid) {
// Submit button disabled
}
if (formState.isDirty) {
// Save/reset enabled
}Pattern: Boolean flags determine allowed actions
5. Jira Workflow (Simplified)
const workflow = {
'Todo': {
transitions: [
{ action: 'start', to: 'In Progress' },
{ action: 'delete', to: null }
]
},
'In Progress': {
transitions: [
{ action: 'complete', to: 'Done' },
{ action: 'block', to: 'Blocked' }
]
},
'Done': {
transitions: []
}
}
function canTransition(currentState, action) {
return workflow[currentState].transitions
.some(t => t.action === action);
}Pattern: Explicit state graph
🎯 Applied to Your Case
Simple Version:
coworker._config.componentTransitions = {
'MainGrid': {
allowedTriggers: [
'onRowClick',
'onNewButton'
]
},
'MainForm': {
allowedTriggers: (run) => {
const triggers = ['onFieldChange'];
if (run.doc.docstatus === 0) {
triggers.push('onSaveButton', 'onDeleteButton');
if (run.output.schema.is_submittable) {
triggers.push('onSubmitButton');
}
}
if (run.doc.docstatus === 1) {
triggers.push('onCancelButton');
}
return triggers;
}
}
}Even Simpler (Frappe-style):
// Just use docstatus + schema flags
function getAllowedUIActions(run) {
const doc = run.doc;
const schema = run.output?.schema;
const component = run.component;
const actions = [];
// Grid actions (always same)
if (component === 'MainGrid') {
return ['onRowClick', 'onNewButton'];
}
// Form actions (depend on docstatus)
if (component === 'MainForm') {
actions.push('onFieldChange');
if (doc.docstatus === 0) {
actions.push('onSaveButton', 'onDeleteButton');
if (schema?.is_submittable) {
actions.push('onSubmitButton');
}
}
if (doc.docstatus === 1 && schema?.is_submittable) {
actions.push('onCancelButton');
}
if (doc.docstatus === 2 && schema?.is_submittable) {
actions.push('onAmendButton');
}
}
return actions;
}🎯 Simplest Pattern (Like Frappe)
// No complex config, just function
coworker.isUIActionAllowed = function(triggerKey, run) {
const doc = run.doc || {};
const schema = run.output?.schema || {};
const component = run.component;
// Grid - always allowed
if (component === 'MainGrid') return true;
// Form field changes - allowed if draft
if (triggerKey === 'MainForm.onFieldChange') {
return doc.docstatus === 0;
}
// Save - allowed if draft
if (triggerKey === 'MainForm.onSaveButton') {
return doc.docstatus === 0;
}
// Submit - allowed if draft + submittable
if (triggerKey === 'MainForm.onSubmitButton') {
return doc.docstatus === 0 && schema.is_submittable === 1;
}
// Cancel - allowed if submitted + submittable
if (triggerKey === 'MainForm.onCancelButton') {
return doc.docstatus === 1 && schema.is_submittable === 1;
}
return false;
};
// In handleUITrigger:
coworker.handleUITrigger = function(triggerKey, context) {
// Check if allowed
if (!this.isUIActionAllowed(triggerKey, context.parentRun)) {
return {
success: false,
error: { message: 'Action not allowed in current state' }
};
}
// Execute
const trigger = this._config.uiTriggers[triggerKey];
const params = trigger.buildRunParams(context);
return this.run(params);
};🎯 Recommendation
Don't overcomplicate with state machine framework.
Use Frappe's pattern:
- Simple function checks:
isActionAllowed(action, docstatus, schema) - Boolean flags:
is_submittable,docstatus,read_only - Guard at trigger time: check before invoking run()
Like Frappe does:
if doc.docstatus == 0:
show_button("Submit")
elif doc.docstatus == 1:
show_button("Cancel")Your equivalent:
if (isUIActionAllowed('onSubmitButton', run)) {
// Show button / enable action
}Simple, clear, no framework needed. Just like Frappe's document lifecycle.
Is this the simplification you were looking for?
The user is asking a very fundamental question about the state machine and where things are documented/tracked. Let me think through this carefully:
- Initial state: run(takeone) executes, status = completed, output.data has the document
- Then user starts editing in MainForm
- Question: What state is this? Where is it documented/tracked?
This is a critical architectural question. Let me think about the states:
Current implementation (from earlier discussion):
- run() executes with draft: true/false
- If draft: true, changes accumulate in run.input.data
- run.doc getter points to either input.data or output.data
But the question is about STATE tracking:
After takeone completes:
- run.status = 'completed'
- run.output.data = [document]
- User starts editing...
Where is the "editing" state tracked? The user is right - there might be blind zones here.
Possible states:
- Initial: run not started
- Running: operation executing
- Completed: operation finished, has output
- Editing: user is making changes (draft mode)
- Saving: changes being persisted
- Error: operation failed
The user is pointing out that "editing" might not be explicitly tracked. If run.status = 'completed', but user is typing in fields, what indicates the document is being modified?
Current approach seems to use:
- run.options.draft flag (indicates if in draft mode)
- run.input.data accumulator (has changes if any)
- run._saving flag (temporary state during save)
But there's no explicit run.status = 'editing' state.
The blind zone:
- run.status stays 'completed' even while user is editing
- No clear state indicating "document is being modified"
- Changes exist in run.input.data but status doesn't reflect this
This is what the user wants to clarify - a complete state machine with all states explicitly defined and tracked.
I'll explore the conceptual challenges of state tracking without diving into specific implementation details. The core issue is how to represent the nuanced states of a document's lifecycle, especially during user interaction. We need a more sophisticated approach that captures the dynamic nature of document editing beyond simple binary flags.
The key challenge is creating a state model that accurately reflects the document's current condition - from initial creation through editing, saving, and potential modifications. This requires thinking about state as a more fluid, multi-dimensional concept rather than a rigid, linear progression.
🎯 You've Identified a Critical Gap
📊 Current State Tracking (Unclear)
After run(takeone) completes:
run.status = 'completed'
run.output.data = [document]
run.options.draft = true (maybe?)User starts editing:
run.status = ??? still 'completed'?
run.input.data = { field: 'new value' } ← Changes here
run.doc = ??? points where?Where is "editing" state documented?
🤔 The Blind Zones
Zone 1: Document Lifecycle State
Status 'completed' doesn't distinguish:
- Viewing (just loaded, no edits)
- Editing (has unsaved changes)
- Saving (persisting changes)
- Saved (changes persisted)Zone 2: Run Lifecycle vs Document Lifecycle
Run lifecycle:
- pending → running → completed → error
Document lifecycle:
- loaded → editing → saving → saved → submitted → cancelled
These are DIFFERENT lifecycles mixed togetherZone 3: Where Changes Live
Changes accumulate in run.input.data
But run.status doesn't reflect this
How do you know if document is "dirty"?💡 What Needs Definition
1. Document States (Not Run States)
Document can be:
- pristine (loaded, no changes)
- dirty (has unsaved changes)
- saving (changes being persisted)
- saved (changes persisted)
- submitted (workflow state)
- cancelled (workflow state)2. Run States
Run can be:
- pending (not started)
- executing (operation in progress)
- completed (operation finished)
- failed (operation errored)3. Edit Session States
Edit session can be:
- inactive (not editing)
- active (editing in progress)
- validating (checking rules)
- persisting (saving to DB)🎯 Key Questions
Q1: Is "editing" a run state or document state?
When user types in field:
- Does run.status change?
- Or is there separate document.state?
- Or is it tracked via run.input.data presence?
Q2: What indicates "dirty" document?
Object.keys(run.input.data).length > 0?- Explicit
run.isDirtyflag? - Separate document state tracker?
Q3: When does parent run "close"?
- After takeone completes?
- After user saves?
- When user navigates away?
- Never (stays open as context)?
Q4: What's the relationship between:
run.status (operation status)
doc.docstatus (Frappe workflow status)
edit session state (UI state)💭 The Core Issue
You're mixing three state machines:
- Run execution state (pending → running → completed)
- Document workflow state (draft → submitted → cancelled)
- Edit session state (pristine → dirty → saving → saved)
They need clear separation and documentation.
🎯 What Needs to Be Defined
Complete State Machine Should Cover:
- How run() execution relates to document lifecycle
- How editing state is tracked and where
- What triggers state transitions
- What's allowed in each state
- How parent-child runs relate to states
- When states reset vs persist
❓ Fundamental Decision Needed
Should the parent run object carry edit session state?
Option A: Run is immutable after completion
- takeone completes → run.status = 'completed' (frozen)
- Editing creates new sibling/child runs
- Each edit = new run
- No "dirty" state on parent
Option B: Run carries edit session
- takeone completes → run.status = 'completed'
- User edits → run.editState = 'dirty'
- run.input.data accumulates changes
- Save creates child, updates parent state
- Parent run tracks entire edit session
Which model do you want?
This is the foundational question that needs answering before defining the state machine.
The user is asking me to evaluate a specific flow pattern they're considering:
- run(select/takeone) → success → render MainForm
- onChange → new run(update, same params, NO render: true, empty input)
- Pass to execute pipeline where controller decides
- Result: immediate update on onChange
Let me analyze this:
Current concern: They want to know if this is correct.
What they're proposing:
- Each field change creates a NEW run
- The new run has operation: 'update'
- It doesn't render (render: false)
- Input is empty (?)
- Controller decides what to do
Issues I see:
- Input empty? - If input is empty, how does controller know what changed? This seems wrong. The input should contain the changed field.
- Immediate update - They say "immediate update on onChange" - this means every keystroke creates a new run that executes update operation. This could be very expensive.
- No accumulation - This bypasses the draft/accumulation pattern we discussed. Every change is immediately processed.
- Controller decides - They say controller will decide, but decide what? Whether to allow? Whether to save to DB?
What I think they're trying to achieve:
- Clean separation: UI just triggers runs
- Controller has all logic
- Every action goes through pipeline
But the issues:
- Empty input makes no sense
- Immediate run per keystroke is expensive
- Where does draft accumulation fit?
- How does this relate to docstatus/schema flags?
Let me evaluate this properly without giving code, focusing on the architectural implications.
🔍 Evaluating Your Proposed Flow
📊 Your Proposed Pattern
1. run(takeone)
→ success
→ render: true
→ MainForm rendered
2. User types in field (onChange)
→ new run(update, same doctype, render: false, input: {empty})
→ execute pipeline
→ controller decides
3. Immediate update happens❌ Critical Issues
Issue 1: Input Empty?
onChange → run(update, input: {empty})Problem: How does controller know what changed if input is empty?
Should be:
onChange → run(update, input: { [field]: value })Issue 2: "Immediate Update" - Update What?
"immediate update on onChange"Unclear what updates:
- Update DB immediately? (No draft, every keystroke saves)
- Update run.output.data? (Parent run state)
- Update UI only? (Re-render)
Issue 3: Every Keystroke = New Run
User types "hello" in field
→ run(update) for 'h'
→ run(update) for 'he'
→ run(update) for 'hel'
→ run(update) for 'hell'
→ run(update) for 'hello'5 database queries for one word?
This is expensive unless controller has debouncing/batching logic.
Issue 4: "Controller Decides" - Decides What?
"pass to execute pipeline where controller will decide"What does controller decide?
- Whether to save to DB?
- Whether to accumulate in memory?
- Whether to validate?
- Whether to allow edit?
This needs to be explicit in the flow.
Issue 5: Where is Draft Accumulation?
Previously discussed: run.input.data accumulates changes
Now: each onChange creates new run(update)These are conflicting patterns:
- Accumulation = changes pile up in one run
- New run per change = no accumulation
Which one?
🎯 What I Think You Mean
Interpretation A: Immediate DB Write
1. run(takeone) → loads document
2. onChange → run(update) → writes to DB immediately
3. No draft, no accumulation
4. Like spreadsheet: every cell edit savesCharacteristics:
- Simple state machine
- No "dirty" state
- Every change persisted
- Controller checks if allowed before DB write
Good for: Grid inline editing Bad for: Form with many fields (too many DB writes)
Interpretation B: Validation Only
1. run(takeone) → loads document
2. onChange → run(update, render: false)
→ controller validates
→ doesn't save to DB
→ updates parent run.input.data
3. Accumulates changes
4. Separate save action writes to DBCharacteristics:
- Controller validates on each change
- But doesn't persist yet
- Accumulates in parent run
- Save button triggers actual DB write
Good for: Forms with validation Bad for: Adds complexity (why not validate directly?)
Interpretation C: Controller Router
1. run(takeone) → loads document
2. onChange → run(update, render: false)
→ controller checks: draft mode?
→ if yes: accumulate in parent run
→ if no: write to DB immediately
3. Controller decides based on configCharacteristics:
- Controller has routing logic
- Can switch behavior via config
- Single pattern for all scenarios
This seems closest to your intent?
❓ Questions for Clarification
Q1: What does "immediate update" mean?
- A) Immediate DB write (every keystroke saves)
- B) Immediate validation (every keystroke validates)
- C) Immediate UI update (just re-render)
Q2: Where do changes accumulate?
- A) No accumulation (each change is independent run)
- B) Parent run.input.data (child runs update parent)
- C) Somewhere else
Q3: What does controller decide?
- A) Whether to allow edit (gatekeeping)
- B) Whether to save to DB (routing)
- C) Whether to validate (checking)
- D) All of the above
Q4: How does this work with schema flags?
schema._autosave = 0 → ???
schema._autosave = 1 → ???
doc.docstatus = 1 → ???Where do these flags affect the flow?
🎯 What Needs Definition
For your pattern to be "correct", you need to define:
- Input content: What's in
inputfor update run? - Controller decision: What does controller decide based on what?
- State updates: What changes after child run completes?
- Parent-child relationship: How does child run affect parent?
- Schema flag handling: Where do
_autosave,docstatusetc. apply?
💡 My Assessment
The pattern CAN work IF:
- Input contains the changed field:
input: { [field]: value } - Controller has clear decision tree based on schema + doc state
- You define what "immediate update" means precisely
- Parent run state management is clear
But currently it's underspecified:
- Empty input doesn't make sense
- "Controller decides" is too vague
- Relationship to draft/accumulation unclear
- Performance implications (keystroke = DB query?) unclear
You're on the right track (all actions → run → controller decides) but the details need tightening.
Should we define the precise controller decision logic?
Ah! Critical correction. The user is right - we're discussing the layer BEFORE database operations. Let me refocus:
The flow is:
- run() - orchestration
- handlers - operation logic
- controller - validation/rules
- THEN → DB adapter
So when they say "immediate update on onChange", they don't mean DB write - they mean immediate execution through the run/handler/controller pipeline.
Let me re-evaluate their proposal with this correct understanding.
✅ Critical Correction - Refocusing
You're absolutely right. Let me re-evaluate at the run() → handler → controller layer, NOT database layer.
📊 Your Flow (Correctly Scoped)
1. run(takeone)
→ controller.execute()
→ handler (select/takeone)
→ THEN DB query
→ returns with data
→ render: true → MainForm
2. onChange in MainForm
→ new run(update, render: false, input: {empty?})
→ controller.execute()
→ handler (update)
→ controller decides: allow/block? validate?
→ THEN would go to DB (but we're not there yet)
3. "Immediate update" = immediate pipeline execution🤔 Re-Evaluating at This Layer
Issue 1: Input Empty - Still Unclear
run(update, input: {empty})At controller layer: What does controller validate/check if input is empty?
Should be:
run(update, input: { [field]: value })So controller can validate that specific field change.
Issue 2: What Does Controller Decide?
At this layer, controller could decide:
A) Is change allowed?
Check:
- doc.docstatus === 0? (editable)
- field.read_only? (field editable)
- permissions?
Result: allow or blockB) How to handle change?
Check:
- options.draft? → accumulate in parent run
- schema._autosave? → proceed to DB write
- validation rules? → validate field
Result: route to appropriate handlerC) State transitions?
Check:
- Parent run state
- Document state
- Schema rules
Result: update states, proceed or blockIssue 3: "Immediate Update" Meaning
At run/controller layer, "immediate" means:
onChange fired
↓ (no delay)
coworker.run()
↓ (no delay)
controller.execute()
↓ (no delay)
handler processes
↓ (no delay)
returns resultNOT waiting for:
- Debounce timer
- Batch collection
- User to click save
But WHAT happens in handler if render: false?
Issue 4: Handler Behavior Without Render
run(update, render: false)
→ controller allows it
→ handler.update() executes
→ handler does... what?Options:
A) Update parent run state
handler finds parent run
handler writes to parent.input.data[field] = value
returns success
(no DB, no render)B) Validate only
handler validates input
returns validation result
(no DB, no state change, no render)C) Prepare for DB
handler builds DB query parameters
stores in run_doc
doesn't execute yet
(no DB yet)Which one?
Issue 5: Parent-Child at Run Layer
Parent: run(takeone, draft: true)
Child: run(update, parent_run_id: xxx)
What happens at run/controller layer:
- Child accesses parent run object?
- Child updates parent.input.data?
- Child validates against parent.output.schema?
- Parent state changes?This relationship needs definition at run layer.
🎯 What Your Pattern Needs to Define (Pre-DB Layer)
1. Input Content
onChange → run(update, input: ?)
Should be:
input: { [fieldname]: value }
Or empty means something specific?2. Controller Decision Tree
controller.execute(run_doc)
↓
Check schema flags
Check doc state
Check operation config
↓
Decision: allow/block/route
↓
If allow: call handlerWhat's the decision logic?
3. Handler Behavior (render: false)
handler.update(run_doc) with render: false
↓
What does handler do?
↓
Return what?4. Parent Run Updates
If child run succeeds:
↓
Update parent run.input.data?
Update parent run.status?
Update parent run state machine?5. When Does Run Complete?
run(update, render: false) completes when:
- Handler returns?
- State is updated?
- Validation passes?✅ Possible Correct Flow
Let me propose what I think you mean:
1. run(takeone, render: true)
→ controller: checks permissions, loads schema
→ handler: queries DB, returns data
→ run_doc.status = 'completed'
→ run_doc.output = { data, schema }
→ renders MainForm
2. onChange
→ run(update, input: {field: value}, render: false, parent_run_id: xxx)
→ controller: checks if allowed
- doc.docstatus === 0? ✓
- field.read_only? ✓
- permissions? ✓
→ handler: updates parent.input.data[field] = value
→ run_doc.status = 'completed'
→ returns { success: true }
→ NO render, NO DB yet
3. After debounce or save button:
→ controller.autoSave() or controller.save()
→ THEN handler queries DB
→ THEN state persistedIs this the flow?
❓ Key Question
At the run/handler/controller layer, when onChange triggers run(update):
Does the handler:
- A) Just validate and return (no state change)
- B) Update parent run.input.data (accumulate change)
- C) Stage change somewhere else
- D) Something else
This is the critical piece to define.
OK, so now we're evaluating the flow with the correct input format:
onChange → run(update, input: { [field]: value }, render: false, parent_run_id: xxx)Let me re-evaluate the entire flow with this correction.
The user wants to understand what happens at the run/handler/controller layer (BEFORE DB).
Let me trace through:
- run(takeone) executes
- controller.execute()
- handler loads from DB
- returns with data + schema
- render: true → MainForm displayed
- run.status = 'completed'
- run.output = { data: [doc], schema: {...} }
- User types in field
- onChange fires
- run(update, { input: { fieldname: newValue }, render: false, parent_run_id: parent.name, source_doctype: parent.source_doctype })
- New run created
- Goes to controller.execute()
- Controller checks:
- Is this allowed?
- docstatus check
- field read_only check
- permissions
- If allowed, calls handler.update()
- Handler does what?
- With render: false
- With input: { field: value }
- With parent_run_id
- Returns result
- What happens next?
- If controller.autoSave enabled → creates another child run to actually save
- If not → waits for manual save
Let me structure this evaluation properly.
✅ Re-Evaluating With Correct Input
📊 Your Flow (With input: { [field]: value })
1. run(takeone, doctype: 'Customer')
→ controller.execute()
→ handler.takeone()
→ queries DB
→ returns: run.output = { data: [doc], schema }
→ render: true → MainForm displayed
→ run.status = 'completed'
2. User types in field (onChange)
→ run(update, {
source_doctype: 'Customer',
input: { website: 'http://example.com' },
parent_run_id: parent.name,
render: false
})
→ New run created
→ controller.execute()
3. Controller checks:
✓ doc.docstatus === 0?
✓ field.read_only?
✓ permissions?
4. If allowed → handler.update()
5. Handler.update() does ???
6. Returns result🎯 Key Evaluation Points
✅ GOOD: Input Has Content
input: { website: 'http://example.com' }Controller and handler now know:
- What changed
- New value
- Can validate this specific field
✅ GOOD: Parent Context Preserved
parent_run_id: parent.nameChild run can access:
- Parent's schema
- Parent's original data
- Parent's state
❓ UNCLEAR: What Does Handler Do?
With render: false and input present, handler.update() could:
Option A: Accumulate in Parent
handler.update(run_doc) {
const parent = CoworkerState.getRun(run_doc.parent_run_id);
// Merge input into parent's draft
parent.input.data = {
...parent.input.data,
...run_doc.input
};
// Validate field
const field = parent.output.schema.fields.find(f => f.fieldname === 'website');
const error = validators.validateField(field, run_doc.input.website);
if (error) {
return { success: false, error };
}
return { success: true };
}Characteristics:
- Parent run accumulates all changes
- Child run just validates + updates parent
- No DB operation
- Parent run.input.data grows with each change
Option B: Standalone Validation
handler.update(run_doc) {
const schema = await getSchema(run_doc.source_doctype);
const field = schema.fields.find(f => f.fieldname === 'website');
// Just validate, don't store anywhere
const error = validators.validateField(field, run_doc.input.website);
if (error) {
return { success: false, error };
}
return { success: true };
}Characteristics:
- Stateless validation
- Doesn't update parent
- Doesn't persist anywhere
- Just validates and returns
Option C: Stage for Later Save
handler.update(run_doc) {
// Don't execute yet, just prepare
// Store in child run for later execution
run_doc._prepared = true;
run_doc._dbOperation = {
table: run_doc.source_doctype,
where: { name: run_doc.query.where.name },
data: run_doc.input
};
return { success: true, staged: true };
}Characteristics:
- Prepares DB operation
- Doesn't execute
- Stores in run object
- Actual DB write happens later
❓ UNCLEAR: When Does DB Write Happen?
With your pattern:
onChange → run(update, render: false)
→ handler doesn't write to DB
Then what triggers DB write?Options:
A) Debounced Auto-Save
After 300ms of no changes:
→ controller.autoSave(parent)
→ Creates child run(update, render: true)
→ This one DOES write to DBB) Manual Save Button
User clicks Save:
→ controller.save(parent)
→ Takes accumulated parent.input.data
→ Creates child run(update)
→ Writes to DBC) Immediate on Blur
Field onBlur:
→ run(update, render: false) already completed
→ If schema._autosave === 1
→ Immediately run another child to write DB❓ UNCLEAR: Where Is "Immediate Update"?
You said "immediate update on onChange" - what updates?
Option A: Parent Run State
onChange → child run → updates parent.input.data
Parent run now shows dirty state
UI can reflect unsaved changesOption B: UI Re-render
onChange → child run → triggers parent re-render
Even though render: false on child
Parent form re-renders to show validationOption C: Nothing Updates
onChange → child run → just validates
Returns success/error
Component manages local state
No global state change🎯 What This Pattern Achieves
✅ Clear Benefits:
- Every action goes through run()
- Consistent pipeline
- Centralized control
- Auditability
- Controller is gatekeeper
- Checks permissions
- Validates rules
- Enforces state machine
- Parent context preserved
- parent_run_id chains runs
- Schema available
- Original data available
- Separation of concerns
- Field component just calls run()
- Controller decides allow/block
- Handler manages state/validation
❓ Still Needs Definition:
- Handler behavior when render: false
- Update parent state?
- Just validate?
- Stage operation?
- Where changes accumulate
- parent.input.data?
- Nowhere (component manages)?
- Separate accumulator?
- When DB write occurs
- Debounced?
- Manual?
- Immediate after validation?
- What "immediate" means
- Immediate validation?
- Immediate state update?
- Immediate pipeline execution?
💡 My Recommendation
Option A (Accumulate in Parent) makes most sense:
onChange:
→ run(update, input: {field: value}, render: false, parent_run_id)
→ controller checks: allowed?
→ handler updates: parent.input.data[field] = value
→ handler validates: field value
→ returns: {success: true/false, error?}
Then separately:
→ If schema._autosave === 1: debounced save
→ Or manual save button: explicit save
→ That creates new run(update, render: true) → writes DBThis gives you:
- ✅ Every action through run()
- ✅ Controller gatekeeping
- ✅ Draft accumulation
- ✅ Deferred DB writes
- ✅ Clear state tracking (parent.input.data)
Is this what you're aiming for?
The user wants a complete, detailed function flow starting from when the second run(update) is triggered (the onChange event). They want to see exactly what happens step by step through the entire pipeline.
Let me trace through based on the pattern we've been discussing:
- User types in field (onChange event)
- Component calls run(update, ...)
- run() function executes
- _resolveAll() resolves parameters
- Construct run_doc
- controller.execute(run_doc)
- Controller checks various things
- Calls handler
- Handler processes
- Returns result
- Updates parent (maybe)
- Returns to component
I should make this very detailed and step-by-step, showing all the decision points, what gets checked, what gets updated, etc.
📋 Complete Function Flow After run(update) Starts
🔄 Full Execution Path
PHASE 1: USER ACTION
1. User types in MainForm field (website field)
value: "http://example.com"
2. FieldData.onChange fires
3. Component calls:
coworker.run({
operation: 'update',
source_doctype: 'Customer',
input: { website: 'http://example.com' },
query: { where: { name: 'CUST-001' }},
parent_run_id: 'run-abc123',
options: { render: false }
})PHASE 2: RUN() INITIALIZATION
4. coworker.run(op) starts
start = Date.now()
5. Validation check:
if (!op?.operation) → would fail (we have 'update')
✓ Pass
6. Call: resolved = this._resolveAll(op)
_resolveAll() executes:
6.1. Resolve operation alias:
'update' → check operationAliases
→ 'update' (no change)
6.2. Resolve doctype:
source_doctype: 'Customer' → check doctypeAliases
→ 'Customer' (no change)
target_doctype: null
6.3. Resolve view:
operation: 'update' → operationToView config
→ view: 'form'
6.4. Get view config:
view: 'form' → views['form']
→ component: 'MainForm'
→ container: 'main_container'
→ options: { render: true }
6.5. Set owner:
→ owner: 'system' (default)
Return resolved: {
operation: 'update',
source_doctype: 'Customer',
target_doctype: null,
view: 'form',
component: 'MainForm',
container: 'main_container',
options: { render: true },
owner: 'system'
}
7. Merge options:
mergedOptions = {
...resolved.options, // { render: true }
...op.options // { render: false }
}
→ { render: false } // User override winsPHASE 3: RUN_DOC CONSTRUCTION
8. Construct run_doc:
{
// Frappe fields
doctype: "Run",
name: generateId("run"), // "run-xyz789"
creation: 1736340000000,
modified: 1736340000000,
modified_by: "system",
docstatus: 0,
owner: "system",
// Operation
operation: "update",
operation_original: "update",
source_doctype: "Customer",
target_doctype: null,
// UI
view: "form",
component: "MainForm",
container: "main_container",
// Data
query: { where: { name: 'CUST-001' }},
input: { website: 'http://example.com' },
output: null,
// State
status: "pending",
success: false,
error: null,
duration: 0,
// Relationships
parent_run_id: "run-abc123",
child_run_ids: [],
// Options
options: { render: false },
// Methods
child: [Function]
}
9. Check draft flag:
if (mergedOptions.draft === undefined) {
opConfig = operations['update']
→ draft: true
run_doc.options.draft = true
}
→ run_doc.options = { render: false, draft: true }
10. Initialize input.data (if draft):
if (options.draft) {
if (!run_doc.input.data) {
run_doc.input.data = {}
}
}
→ run_doc.input.data = {}
11. Define doc getter:
Object.defineProperty(run_doc, "doc", {
get: function() {
return this.options.draft
? this.input.data
: this.output?.data?.[0];
}
})
12. Define child() method:
run_doc.child = async function(childOp) { ... }PHASE 4: CONTROLLER EXECUTION
13. Store run in CoworkerState:
CoworkerState.addRun(run_doc)
14. Call controller:
result = await coworker.controller.execute(run_doc)
controller.execute() starts:
14.1. Extract parameters:
operation = 'update'
options = { render: false, draft: true }
14.2. Check draft flag (already set):
✓ Already set to true
14.3. Check skipController:
if (options.skipController) → false
✓ Continue
14.4. Get operation config:
opConfig = operations['update']
→ {
type: "write",
draft: true,
requiresSchema: true,
validate: true,
fetchOriginals: true
}
14.5. Fetch schema (if needed):
if (opConfig.requiresSchema && !options.skipSchema) {
doctype = run_doc.source_doctype // 'Customer'
if (!run_doc.output) {
run_doc.output = {}
}
if (!run_doc.output.schema) {
schema = await coworker.getSchema('Customer')
run_doc.output.schema = schema
}
}
→ run_doc.output.schema = { ...Customer schema }
14.6. Check operation type:
opConfig.type === 'write' → true
✓ Route to _processWrite()PHASE 5: WRITE PROCESSING
15. controller._processWrite(run_doc, opConfig) starts:
15.1. Extract parameters:
operation = 'update'
input = { website: 'http://example.com' }
query = { where: { name: 'CUST-001' }}
15.2. Get doctype:
doctype = run_doc.source_doctype // 'Customer'
15.3. Get schema:
schema = run_doc.output?.schema // Already loaded
15.4. Fetch originals (if config says so):
if (opConfig.fetchOriginals && query?.where) {
filter = coworker._buildPrismaWhere('Customer', { name: 'CUST-001' })
result = await coworker._dbQuery({ filter })
items = result.data
if (items.length === 0) {
return { success: true, output: { data: [], schema, meta: {...}}}
}
}
→ items = [{ name: 'CUST-001', customer_name: 'Jim', ... }]
15.5. Validate (if config says so):
if (opConfig.validate) {
inputData = input // { website: 'http://example.com' }
// For update with originals - validate merged
if (items.length > 0) {
for (item of items) {
merged = { ...item, ...inputData }
validation = this._validate(merged, schema)
if (!validation.valid) {
return { success: false, errors: validation.errors }
}
}
}
}
_validate() executes:
15.5.1. Check schema exists:
✓ schema present
15.5.2. Loop through schema fields:
for each field in schema.fields:
- customer_name field:
value = merged.customer_name = 'Jim'
reqd = true
error = validators.validateField(field, 'Jim')
→ null (valid)
- website field:
value = merged.website = 'http://example.com'
reqd = false
error = validators.validateField(field, 'http://example.com')
→ null (valid)
... (all fields checked)
15.5.3. Return validation result:
→ { valid: true, errors: [] }
15.6. Store items in run_doc:
run_doc._items = itemsPHASE 6: HANDLER EXECUTION
16. Execute handler:
result = await coworker._handlers['update'](run_doc)
handler.update() starts:
16.1. Extract parameters:
source_doctype = 'Customer'
input = { website: 'http://example.com' }
query = { where: { name: 'CUST-001' }}
options = { render: false, draft: true }
16.2. Get items (from controller):
items = run_doc._items
→ [{ name: 'CUST-001', customer_name: 'Jim', ... }]
16.3. Check if items exist:
if (!items || items.length === 0) {
→ Would return error
}
✓ Items exist
16.4. Process each item:
for (item of items) {
merged = { ...item, ...input }
→ {
name: 'CUST-001',
customer_name: 'Jim',
website: 'http://example.com', // ← Updated
...other fields
}
}
16.5. Check draft mode:
if (options.draft) {
// ❓ DECISION POINT: What happens here?
// OPTION A: Update parent run
if (run_doc.parent_run_id) {
parent = CoworkerState.getRun(run_doc.parent_run_id)
if (parent) {
parent.input.data = {
...parent.input.data,
...input
}
→ parent.input.data.website = 'http://example.com'
}
}
return {
success: true,
output: {
data: [merged],
schema: run_doc.output?.schema
}
}
}
// OPTION B: Would execute DB write (but draft: true, so skip)
16.6. Return result:
→ {
success: true,
output: {
data: [{
name: 'CUST-001',
customer_name: 'Jim',
website: 'http://example.com',
...
}],
schema: { ...schema }
}
}PHASE 7: RESULT PROCESSING
17. Back in controller.execute():
17.1. Receive handler result:
result = { success: true, output: {...} }
17.2. Return result:
return result
18. Back in run():
18.1. Receive controller result:
result = { success: true, output: {...} }
18.2. Merge into run_doc:
run_doc.output = result.output
run_doc.success = result.success
run_doc.error = result.error
run_doc.status = 'completed'
run_doc.duration = Date.now() - start
19. Check render flag:
if (mergedOptions.render) {
→ false, skip render
}
20. Update CoworkerState:
CoworkerState.updateRun(run_doc)
21. Return run_doc:
return run_docPHASE 8: PARENT STATE UPDATE (if Option A in handler)
22. Parent run state after child completes:
Parent run (run-abc123):
{
...
input: {
query: { where: { name: 'CUST-001' }},
data: {
website: 'http://example.com' // ← Accumulated from child
}
},
output: {
data: [{
name: 'CUST-001',
customer_name: 'Jim',
website: 'old-value', // ← Original, unchanged
...
}],
schema: { ... }
},
status: 'completed',
options: { render: true, draft: true }
}
Child run (run-xyz789):
{
...
parent_run_id: 'run-abc123',
input: { website: 'http://example.com' },
output: {
data: [{ ...merged with new website }],
schema: { ... }
},
status: 'completed',
options: { render: false, draft: true }
}PHASE 9: COMPONENT RECEIVES RESULT
23. Back in FieldData.onChange:
23.1. run() promise resolves:
childRun = { success: true, output: {...}, ... }
23.2. Component can check:
if (!childRun.success) {
// Show error
}
if (childRun.success) {
// Change accepted
// Parent now has change in input.data
}
23.3. No re-render triggered (render: false)🎯 Summary of Key Points
What Happened:
- ✅ New run created for update operation
- ✅ Parent context preserved (parent_run_id)
- ✅ Schema loaded (already cached or fetched)
- ✅ Original data fetched (fetchOriginals: true)
- ✅ Validation executed (merged data validated)
- ✅ Handler processed update
- ✅ Parent run.input.data updated with change
- ✅ No render triggered
- ✅ No DB write (draft mode)
State After:
- Parent: has accumulated change in input.data
- Child: completed successfully
- Original data: unchanged in parent.output.data
- UI: not re-rendered (render: false)
Next Steps Would Be:
- More onChange events → more children → accumulate in parent
- Debounce timer → controller.autoSave() → DB write
- Or save button → controller.save() → DB write
Is this the flow you wanted to see?
The user is pointing out that in the actual code, there's an _exec function that routes ALL operations through controller.execute(). They're confirming that nothing bypasses the controller.
Let me verify this in the flow I just described:
In my Phase 4, Step 14, I had:
14. Call controller:
result = await coworker.controller.execute(run_doc)So yes, in my flow description, I showed it going through the controller. But let me make sure I'm understanding their point correctly.
Looking at the _exec code:
- It's a routing function
- It handles adapter switching
- But most importantly: it ALWAYS calls
this.controller.execute(run_doc) - There's no bypass path
So the user is confirming: "Everything goes through controller, right? Nothing bypasses it?"
The answer is: YES, based on this code, everything goes through controller.execute().
But wait, let me check if there are any operations that might bypass controller. Looking back at the controller code they shared earlier:
// ✅ ESCAPE HATCH: Skip controller entirely
if (options.skipController) {
return await coworker._handlers[operation](run_doc);
}Ah! So there IS a bypass mechanism - the skipController option. But it's opt-in, not default.
So the accurate answer is:
- By default: Everything goes through controller
- But: There's an explicit
skipControllerescape hatch - The
_execfunction always calls controller, but controller itself can skip its own logic
Let me clarify this for the user.
✅ Correct - Almost Everything Goes Through Controller
📊 The Actual Flow
coworker.run()
↓
coworker._exec(run_doc)
↓
coworker.controller.execute(run_doc) ← ALWAYS called
↓
Inside controller.execute():
if (options.skipController) {
→ coworker._handlers[operation](run_doc) ← Direct to handler
} else {
→ controller logic → handler
}🎯 So More Precisely
Default Path (99% of cases):
run()
→ _exec()
→ controller.execute()
→ controller checks/validates
→ handler✅ Nothing bypassed
Escape Hatch (explicit opt-in):
run({ ..., options: { skipController: true }})
→ _exec()
→ controller.execute()
→ sees skipController flag
→ SKIPS controller logic
→ directly calls handler⚠️ Controller logic bypassed, but controller.execute() still called
🎯 In Your onChange Flow
onChange
→ run(update, { render: false }) // No skipController
→ _exec()
→ controller.execute()
→ ✅ Goes through ALL controller checks:
- Draft flag set
- Schema loaded
- Validation executed
- Originals fetched
→ handler.update()Nothing bypassed. Full pipeline.
✅ Confirmation
Yes, with your _exec routing function:
- ✅ Every run() calls _exec()
- ✅ Every _exec() calls controller.execute()
- ✅ Controller is the gatekeeper
- ⚠️ Unless explicit skipController: true
Your architecture enforces: All operations → Controller → Handler
The only way to bypass controller logic is explicit skipController: true flag, which still goes through controller.execute() function, just skips the logic inside.
So your statement is correct: Nothing bypasses the controller (by default).
This is a great architectural question. The user is asking about where validation and cross-checking against schema should happen in the controller architecture.
Let me think about the layers:
- Current situation from the flow:
- Controller loads schema (if requiresSchema)
- Controller calls
_processWrite()for write operations _processWrite()validates using_validate()method- Validation happens BEFORE handler execution
- Key architectural questions:
- Where should validation live?
- When should validation happen?
- What validates against schema?
- How to separate concerns?
- Validation types:
- Field-level validation: Individual field rules (type, length, required, etc.)
- Document-level validation: Cross-field rules (end_date > start_date)
- Schema validation: Against schema definition
- Business logic validation: Domain-specific rules
- Permission validation: Can user edit this field/doc?
- State validation: Can this action happen in current state?
- Architectural patterns:
Pattern A: Monolithic Controller
- Controller does everything
- Validation, permissions, state checks all in one place
- Simple but becomes bloated
- Controller coordinates
- Separate validators for different concerns
- Clean separation but more complex
- Controller runs validation pipeline
- Each validator is a stage
- Composable and extensible
- Schema defines validation rules
- Controller executes rules from schema
- Declarative, config-driven
- The three-tier field system they already have:
- Tier 1: System field rules
- Tier 2: Field type handlers
- Tier 3: Custom field rules
Their existing three-tier structure suggests they're already thinking in layers. The key decisions are whether validation belongs in the controller or a separate layer, whether it should block execution or run alongside handlers, and how to structure error handling. I should propose an architecture that extends their current patterns rather than replacing them.
🎯 Controller Architecture for Validation
📊 Current State (What You Have)
controller.execute()
↓
_processWrite()
↓
Validation happens HERE (before handler)
↓
If valid → handler
If invalid → return error🏗️ Recommended Architecture: Validation Pipeline
Conceptual Layers:
controller.execute()
↓
┌─────────────────────────────────────┐
│ LAYER 1: Operation Validation │
│ - Is operation allowed? │
│ - Correct parameters? │
│ - Schema loaded if needed? │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ LAYER 2: State Validation │
│ - Check docstatus │
│ - Check permissions │
│ - Check workflow state │
│ - Can this action happen now? │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ LAYER 3: Schema Validation │
│ - Field types correct? │
│ - Required fields present? │
│ - Field constraints met? │
│ - Uses schema definition │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ LAYER 4: Document Validation │
│ - Cross-field rules │
│ - Business logic │
│ - Custom validations │
│ - Domain-specific rules │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ LAYER 5: Data Processing │
│ - Your 3-tier field system │
│ - System field rules │
│ - Field type handlers │
│ - Custom field rules │
└─────────────────────────────────────┘
↓
handler.execute()🎯 Detailed Validation Architecture
Layer 1: Operation Validation
Purpose: Validate run_doc structure itself
Location: controller.execute() - entry point
Checks:
- operation exists and is known
- required parameters present (doctype, input, etc.)
- schema loaded if requiresSchema: true
- options are valid
Returns:
- Early exit if invalid
- Prevents malformed runs from proceedingLayer 2: State Validation (Gatekeeper)
Purpose: Can this action happen in current state?
Location: controller._validateState(run_doc)
Checks:
- doc.docstatus allows this operation?
- schema.read_only blocks edits?
- field.read_only for specific field?
- user has permissions?
- workflow allows transition?
Returns:
- { allowed: true/false, reason: string }
- Blocks unauthorized/impossible actionsLayer 3: Schema Validation
Purpose: Does data match schema definition?
Location: controller._validateSchema(data, schema)
Checks:
- Field types correct (Data is string, Int is number)
- Required fields present
- Field length constraints
- Min/max values
- Pattern matching (regex)
- Options/Select valid values
Uses:
- schema.fields definitions
- coworker.validators.validateField()
Returns:
- { valid: true/false, errors: [...] }
- Per-field error messagesLayer 4: Document Validation
Purpose: Business logic and cross-field rules
Location: controller._validateDocument(doc, schema)
Checks:
- end_date > start_date
- total = quantity * rate
- dependent field logic
- Domain-specific rules
- Custom validation functions
Returns:
- { valid: true/false, errors: [...] }
- Document-level error messagesLayer 5: Data Processing
Purpose: Transform/normalize before handler
Location: coworker.processDocument(run_doc)
Does:
- Apply system field rules (name, owner, etc.)
- Apply field type handlers (cast, normalize)
- Apply custom field rules (computed fields)
- Prepare data for handler
Returns:
- Processed document
- Ready for handler execution📋 Controller Structure
controller.execute(run_doc) {
// ═══════════════════════════════════════
// LAYER 1: Operation Validation
// ═══════════════════════════════════════
validate run_doc structure
load schema if needed
set draft flag
if (skipController) {
→ handler (escape hatch)
}
// ═══════════════════════════════════════
// ROUTE BY TYPE
// ═══════════════════════════════════════
if (type === 'read') {
→ handler (reads don't need validation)
}
if (type === 'write') {
→ _processWrite(run_doc)
}
}
controller._processWrite(run_doc, opConfig) {
// ═══════════════════════════════════════
// LAYER 2: State Validation (Gatekeeper)
// ═══════════════════════════════════════
stateCheck = _validateState(run_doc)
if (!stateCheck.allowed) {
return {
success: false,
error: { message: stateCheck.reason }
}
}
// ═══════════════════════════════════════
// Fetch originals if needed
// ═══════════════════════════════════════
if (fetchOriginals) {
items = await fetch original docs
run_doc._items = items
}
// ═══════════════════════════════════════
// LAYER 3: Schema Validation
// ═══════════════════════════════════════
if (opConfig.validate) {
// Validate against schema
schemaValidation = _validateSchema(data, schema)
if (!schemaValidation.valid) {
return {
success: false,
errors: schemaValidation.errors
}
}
}
// ═══════════════════════════════════════
// LAYER 4: Document Validation
// ═══════════════════════════════════════
if (opConfig.validate) {
// Cross-field and business rules
docValidation = _validateDocument(data, schema)
if (!docValidation.valid) {
return {
success: false,
errors: docValidation.errors
}
}
}
// ═══════════════════════════════════════
// LAYER 5: Data Processing
// ═══════════════════════════════════════
if (requiresProcessing) {
processedDoc = await coworker.processDocument(run_doc)
}
// ═══════════════════════════════════════
// Execute Handler
// ═══════════════════════════════════════
return await handler(run_doc)
}🎯 Validation Methods
State Validator:
controller._validateState(run_doc) {
const doc = run_doc.doc || run_doc.output?.data?.[0] || {};
const schema = run_doc.output?.schema || {};
const operation = run_doc.operation;
// Check docstatus
if (operation === 'update') {
if (doc.docstatus === 2) {
return {
allowed: false,
reason: 'Cannot edit cancelled document'
};
}
if (doc.docstatus === 1 && !schema.is_submittable) {
return {
allowed: false,
reason: 'Document is submitted'
};
}
}
// Check schema read_only
if (schema.read_only) {
return {
allowed: false,
reason: 'Doctype is read-only'
};
}
// Check field-specific (if updating single field)
if (operation === 'update' && run_doc.input) {
const fieldname = Object.keys(run_doc.input)[0];
const field = schema.fields?.find(f => f.fieldname === fieldname);
if (field?.read_only) {
return {
allowed: false,
reason: `Field ${field.label} is read-only`
};
}
if (doc.docstatus === 1 && !field?.allow_on_submit) {
return {
allowed: false,
reason: `Cannot edit ${field.label} after submission`
};
}
}
return { allowed: true };
}Schema Validator:
controller._validateSchema(doc, schema) {
if (!schema?.fields) {
return { valid: true, errors: [] };
}
const errors = [];
for (const field of schema.fields) {
const value = doc[field.fieldname];
// Use existing validator
const error = coworker.validators.validateField(field, value);
if (error) {
errors.push({
field: field.fieldname,
message: error
});
}
}
return {
valid: errors.length === 0,
errors
};
}Document Validator:
controller._validateDocument(doc, schema) {
const errors = [];
// Custom validation functions from schema
if (schema.validate_functions) {
for (const validateFn of schema.validate_functions) {
const error = validateFn(doc);
if (error) {
errors.push({ message: error });
}
}
}
// Common cross-field validations
// (Could be extended with custom rules)
return {
valid: errors.length === 0,
errors
};
}✅ Benefits of This Architecture
| Aspect | Benefit |
|---|---|
| Separation of Concerns | Each layer has clear responsibility |
| Early Exit | Invalid operations blocked early |
| Reusable Validators | Same validators used across operations |
| Schema-Driven | Validation rules from schema, not hardcoded |
| Extensible | Add custom validators per layer |
| Testable | Each layer can be tested independently |
| Clear Error Messages | Errors from specific layer |
| Performance | Skip expensive checks if early validation fails |
🎯 Integration With Your 3-Tier System
Your existing field processing system fits in Layer 5:
Layer 1: Operation structure ✓
Layer 2: State/permissions ✓
Layer 3: Schema validation ✓
Layer 4: Document validation ✓
Layer 5: Field processing → YOUR EXISTING 3-TIER SYSTEM
├─ Tier 1: System field rules
├─ Tier 2: Field type handlers
└─ Tier 3: Custom field rulesThis means:
- Validation happens BEFORE field processing
- Field processing happens on VALID data
- Handler receives VALIDATED + PROCESSED data
🎯 Summary
Correct Architecture:
- Controller orchestrates validation pipeline
- 5 validation layers, each with clear purpose
- Early exit on validation failure
- Schema-driven rules, not hardcoded
- Your 3-tier field system = Layer 5 (processing)
- Handler receives clean, validated, processed data
Every validation check happens BEFORE handler execution.
Is this the validation architecture you need?
The user is asking about how Zod validates data against schemas. This is a good question because Zod is a popular TypeScript-first schema validation library, and understanding its approach could inform how they should architect their validation system.
Let me explain Zod's core concepts and patterns:
- Schema Definition
- Parse/Validate
- Type Inference
- Error Handling
- Transformations
- Refinements
This will help them understand if they should adopt a similar pattern or what principles they can learn from Zod.
📋 How Zod Validates - Core Concepts
🎯 Zod's Pattern
1. Schema Definition (Declarative)
const UserSchema = z.object({
name: z.string().min(1),
email: z.string().email(),
age: z.number().int().min(0).max(120),
role: z.enum(['admin', 'user', 'guest']),
isActive: z.boolean().default(true)
});Key principle: Schema is a data structure, not functions
2. Validation (Parse Method)
// Synchronous validation
const result = UserSchema.safeParse(data);
if (result.success) {
// result.data is typed and validated
console.log(result.data.name);
} else {
// result.error contains validation errors
console.log(result.error.issues);
}
// Or throw on error
const validData = UserSchema.parse(data); // Throws if invalidKey principle: Separate schema from validation execution
3. Error Structure (Flat Array)
{
success: false,
error: {
issues: [
{
path: ['email'],
message: 'Invalid email',
code: 'invalid_string'
},
{
path: ['age'],
message: 'Number must be greater than 0',
code: 'too_small'
}
]
}
}Key principle: All errors returned at once, not fail-fast
4. Chaining (Fluent API)
z.string()
.min(3, "Too short")
.max(50, "Too long")
.email("Invalid email")
.transform(s => s.toLowerCase())
.refine(s => !s.includes('spam'), "No spam")Key principle: Validation rules chain together
5. Custom Refinements (Business Logic)
const PasswordSchema = z.string()
.min(8)
.refine(
(val) => /[A-Z]/.test(val),
{ message: "Must contain uppercase letter" }
)
.refine(
(val) => /[0-9]/.test(val),
{ message: "Must contain number" }
);Key principle: Schema + custom validators in one definition
6. Cross-Field Validation (Superrefine)
const FormSchema = z.object({
password: z.string(),
confirmPassword: z.string()
}).superrefine((data, ctx) => {
if (data.password !== data.confirmPassword) {
ctx.addIssue({
path: ['confirmPassword'],
message: 'Passwords must match',
code: z.ZodIssueCode.custom
});
}
});Key principle: Document-level validation after field validation
7. Transformations (Data Pipeline)
const DateSchema = z.string()
.transform(str => new Date(str))
.refine(date => !isNaN(date.getTime()), "Invalid date");
// Input: "2024-01-08"
// Output: Date objectKey principle: Validation + transformation in one pipeline
8. Nested Objects
const AddressSchema = z.object({
street: z.string(),
city: z.string(),
zipCode: z.string().regex(/^\d{5}$/)
});
const PersonSchema = z.object({
name: z.string(),
address: AddressSchema // Nested schema
});
```
**Key principle:** Composable schemas
---
## 🎯 **Zod's Architecture Pattern**
```
┌─────────────────────────────────────┐
│ Schema Definition (Declarative) │
│ - Define shape of valid data │
│ - Chain validation rules │
│ - Compose nested schemas │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Parse Execution (Imperative) │
│ - schema.parse(data) │
│ - or schema.safeParse(data) │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Validation Pipeline │
│ 1. Type checking │
│ 2. Field-level rules │
│ 3. Transformations │
│ 4. Custom refinements │
│ 5. Cross-field validation │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Result │
│ success: true → typed data │
│ success: false → error array │
└─────────────────────────────────────┘💡 Key Design Principles
1. Schema as Data
// Schema is an object you can inspect, compose, extend
const schema = z.object({ name: z.string() });
// Can be passed around, stored, merged
const extendedSchema = schema.extend({
age: z.number()
});2. Fail-Late (Collect All Errors)
// Validates ALL fields, returns ALL errors
const result = schema.safeParse(data);
// Not: fail on first error3. Type Safety (TypeScript)
const schema = z.object({ name: z.string() });
type User = z.infer<typeof schema>;
// TypeScript type inferred from schema4. Immutable Schemas
const base = z.string();
const extended = base.min(3); // Returns new schema
// base is unchanged5. Sync + Async Support
// Sync
schema.parse(data);
// Async (for async refinements)
await schema.parseAsync(data);🔄 Comparison to Your Architecture
| Aspect | Zod | Your System |
|---|---|---|
| Schema source | Code definition | Database (Frappe schema) |
| Validation trigger | Explicit parse() call | During controller.execute() |
| Error collection | All errors at once | Can be fail-fast or collect |
| Custom rules | .refine() method | Custom validators per field |
| Cross-field | .superrefine() | controller._validateDocument() |
| Transformations | .transform() | Field type handlers |
| Type safety | TypeScript inference | Runtime only |
🎯 What You Can Learn From Zod
1. Separate Schema from Validation Execution
Zod:
const schema = z.object({ ... }); // Define once
const result = schema.parse(data); // Execute many timesYour equivalent:
const schema = await getSchema('Customer'); // Load once
const result = controller._validateSchema(data, schema); // Execute2. Collect All Errors (Fail-Late)
Zod:
// Returns array of all validation errors
const result = schema.safeParse(data);
result.error.issues // All problemsYour equivalent:
controller._validateSchema(data, schema) {
const errors = [];
// Don't return on first error
for (const field of schema.fields) {
const error = validateField(field, data[field.fieldname]);
if (error) {
errors.push(error); // Collect all
}
}
return { valid: errors.length === 0, errors };
}3. Fluent Validation Builder
Zod:
z.string().min(3).max(50).email()Your equivalent (if you wanted):
field.validate()
.required()
.minLength(3)
.maxLength(50)
.pattern(/^[a-z]+$/)But you already have this in schema definition (from Frappe):
{
fieldname: 'email',
fieldtype: 'Data',
reqd: 1,
length: 50,
options: 'Email'
}4. Custom Refinements
Zod:
.refine(val => customLogic(val), "Error message")Your equivalent:
// In schema or config
customValidators: {
'Customer.email': (value) => {
if (value.endsWith('@spam.com')) {
return 'Spam domains not allowed';
}
return null;
}
}5. Cross-Field Validation
Zod:
.superrefine((data, ctx) => {
if (data.end_date < data.start_date) {
ctx.addIssue({ ... });
}
})Your equivalent:
controller._validateDocument(doc, schema) {
const errors = [];
if (doc.end_date && doc.start_date) {
if (new Date(doc.end_date) < new Date(doc.start_date)) {
errors.push({
fields: ['end_date', 'start_date'],
message: 'End date must be after start date'
});
}
}
return { valid: errors.length === 0, errors };
}✅ Recommended Pattern for Your System
Adopt Zod's principles, but keep schema-driven:
controller._validateSchema(data, schema) {
const errors = [];
// 1. Validate each field (like Zod's field-level)
for (const field of schema.fields) {
const value = data[field.fieldname];
const error = this._validateField(field, value);
if (error) {
errors.push({
path: [field.fieldname],
message: error,
field: field.fieldname
});
}
}
// 2. Apply custom refinements (like Zod's .refine())
if (schema.custom_validators) {
for (const [fieldname, validator] of Object.entries(schema.custom_validators)) {
const error = validator(data[fieldname], data);
if (error) {
errors.push({
path: [fieldname],
message: error,
field: fieldname
});
}
}
}
// Return all errors at once (like Zod's safeParse)
return {
valid: errors.length === 0,
errors: errors
};
}
controller._validateDocument(data, schema) {
const errors = [];
// Cross-field validation (like Zod's .superrefine())
if (schema.document_validators) {
for (const validator of schema.document_validators) {
const error = validator(data);
if (error) {
errors.push({
message: error,
type: 'document'
});
}
}
}
return {
valid: errors.length === 0,
errors: errors
};
}🎯 Summary: Zod's Lessons
- Schema as data structure → You have this (Frappe schema)
- Separate definition from execution → Schema loaded once, validate many times
- Collect all errors → Don't fail on first error
- Flat error array → Easy to display to user
- Chainable rules → Your schema already has this
- Custom refinements → Add custom validator support
- Cross-field validation → Separate document-level validation
- Immutable patterns → Don't modify schema during validation
Your architecture already follows many Zod principles because you're schema-driven!
The user is asking why Zod calls validation "parse" instead of "validate". This is an interesting design decision that reflects a deeper philosophy.
Let me explain Zod's naming rationale:
- Parse implies transformation and validation together
- Parse comes from parsing tradition (JSON.parse, etc.)
- Parse suggests converting unknown data into known/typed data
- Validate suggests just checking (boolean result)
- Parse returns the validated AND typed data, not just true/false
This is a key conceptual difference.
🎯 Why Zod Calls It "Parse" Not "Validate"
💡 The Conceptual Difference
Validate (Traditional)
// Just checks, returns boolean
function validate(data) {
return data.email.includes('@'); // true/false
}
if (validate(data)) {
// Data unchanged, just checked
useData(data);
}Validation = checking only
Parse (Zod's Approach)
// Checks AND transforms AND types
const result = schema.parse(data);
// result IS the validated data
// Not just "is valid?"
// But "here is the clean data"Parse = validate + transform + type
📊 What "Parse" Means
1. Parse = Unknown → Known
// Input: unknown/untrusted data
const untrustedInput = req.body; // Could be anything
// Parse: convert to known/typed data
const user = UserSchema.parse(untrustedInput);
// Output: typed, validated, transformed
user.email // TypeScript knows this is stringLike JSON.parse():
JSON.parse('{"name":"John"}') // string → objectZod parse:
UserSchema.parse(unknownData) // unknown → User type2. Parse Includes Transformation
const schema = z.object({
createdAt: z.string().transform(s => new Date(s)),
age: z.string().transform(s => parseInt(s)),
email: z.string().transform(s => s.toLowerCase().trim())
});
const input = {
createdAt: "2024-01-08",
age: "25",
email: " USER@EXAMPLE.COM "
};
const result = schema.parse(input);
// {
// createdAt: Date object (not string!),
// age: 25 (number, not string!),
// email: "user@example.com" (cleaned!)
// }
```
**Parse changes the data, validate doesn't**
---
### **3. Parse = Data Pipeline**
```
Unknown Data
↓ Type checking
↓ Validation rules
↓ Transformations
↓ Refinements
↓
Typed, Clean DataIt's a parsing/processing pipeline, not just a check
🎯 Zod's API Design
Two Methods:
// 1. parse() - throws on error
try {
const validData = schema.parse(data);
// Use validData
} catch (error) {
// Handle validation error
}// 2. safeParse() - returns result object
const result = schema.safeParse(data);
if (result.success) {
const validData = result.data; // Clean data
} else {
const errors = result.error; // Error details
}Both are "parse" because both return transformed data on success
💭 Why Not "validate()"?
If Zod used validate():
// Would imply just checking
const isValid = schema.validate(data); // true/false?
if (isValid) {
// But data is still untrusted/unchanged
useData(data); // Still unsafe!
}
```
**Problem:** Validation alone doesn't make data safe to use
---
## 🔄 **The Parse Philosophy**
### **Traditional Validation:**
```
Input → Check → Boolean → (Use original input if true)
```
### **Zod Parse:**
```
Input → Check + Transform → Clean Output (or error)You MUST use the output, not the input
🎯 Real-World Example
Traditional Validation Approach:
function validateUser(data) {
if (!data.email || typeof data.email !== 'string') return false;
if (!data.age || typeof data.age !== 'number') return false;
return true;
}
// Usage
if (validateUser(req.body)) {
// Still using untyped req.body
saveUser(req.body); // Unsafe!
}Zod Parse Approach:
const UserSchema = z.object({
email: z.string().email().toLowerCase(),
age: z.number().int().min(0)
});
// Usage
const user = UserSchema.parse(req.body);
// If we get here, user is:
// - Typed correctly
// - Validated
// - Transformed (email lowercased)
// - Safe to use
saveUser(user); // Safe!🎯 Applied To Your System
Current (Validate Pattern):
const validation = controller._validateSchema(data, schema);
if (validation.valid) {
// Still using original data
handler(run_doc);
}Parse Pattern (Zod-inspired):
const parsed = controller._parseData(data, schema);
if (parsed.success) {
// Use parsed.data (cleaned, transformed)
run_doc.input.data = parsed.data; // Not original data
handler(run_doc);
}💡 Your Field System Already Does This!
Your 3-tier field processing IS parsing:
coworker.processDocument(run_doc) {
// Tier 1: System fields (transformation)
doc.name = generateId();
doc.owner = getCurrentUser();
// Tier 2: Field type handlers (transformation)
doc.date_field = new Date(doc.date_field); // string → Date
doc.number_field = Number(doc.number_field); // string → number
// Tier 3: Custom rules (transformation)
doc.total = doc.quantity * doc.rate;
return doc; // Transformed document
}This is parsing, not just validation!
✅ Should You Use "parse" Terminology?
Option A: Keep "validate"
controller._validateSchema()
controller._validateDocument()
coworker.validators.validateField()Pro: Clear, familiar Con: Implies checking only, not transformation
Option B: Use "parse"
controller._parseSchema()
controller._parseDocument()
coworker.parsers.parseField()Pro: Matches Zod's philosophy, implies transformation Con: Less familiar, might confuse
Option C: Hybrid (Recommended)
// Validation = checking only
controller._validateState() // Just checks state
controller._validatePermissions() // Just checks permissions
// Parse = check + transform
controller._parseData() // Validates AND transforms
coworker.processDocument() // Already parses!Pro: Clear distinction between checking vs transforming Con: More methods
🎯 Summary
Why Zod Calls It "Parse":
- ✅ Transformation included - not just checking
- ✅ Unknown → Known - converts untrusted to typed
- ✅ Returns clean data - not just boolean
- ✅ Data pipeline - multiple stages
- ✅ Type safety - output is typed (TypeScript)
Validate vs Parse:
| Aspect | Validate | Parse |
|---|---|---|
| Returns | Boolean or errors | Clean data or errors |
| Transforms | No | Yes |
| Output | Same input | Processed input |
| Usage | if (valid) use(input) | use(output) |
Your System:
- Validation layers = checking (state, permissions)
- Field processing = parsing (transformation)
- Already does both, just not explicitly named
You're already doing what Zod does, just with different terminology!
This is a great question. The user is asking how Zod handles transformations when dealing with existing data that needs to be edited, which is a common real-world scenario.
Let me think about this:
- Zod's transformations are typically applied during parsing, which creates NEW data
- But when you're editing existing data, you have:
- Original data (from DB)
- Partial changes (what user edited)
- Need to merge them
Zod doesn't actually have a built-in "partial update" or "merge with existing" pattern. Let me explain how this is typically handled and the limitations.
Key points:
- Zod parses complete objects, not partial updates
- For partial updates, you typically use
.partial()to make all fields optional - Transformations apply to the input data, not to merging with existing data
- You need to handle the merge logic separately
Let me structure this clearly for the user.
🤔 Zod's Limitation: No Built-In Merge Pattern
❌ What Zod Doesn't Handle
Your Scenario:
// Original from DB
const original = {
name: 'CUST-001',
customer_name: 'John Doe',
email: 'john@example.com',
phone: '555-1234'
};
// User edits one field
const changes = {
email: 'newemail@example.com'
};
// Need: Merge + validate + transform
// Zod doesn't have built-in merge logic🔄 How Zod Approaches This
Option 1: Parse Complete Object
const UserSchema = z.object({
name: z.string(),
customer_name: z.string(),
email: z.string().email().toLowerCase(),
phone: z.string()
});
// You merge manually BEFORE parsing
const merged = { ...original, ...changes };
// Then parse complete object
const result = UserSchema.parse(merged);Zod's role: Parse complete, merged object Your role: Merge before parsing
Option 2: Partial Schema
const UserUpdateSchema = UserSchema.partial();
// All fields now optional
// Parse just the changes
const validatedChanges = UserUpdateSchema.parse(changes);
// You still merge manually
const final = { ...original, ...validatedChanges };Zod validates partial input, you handle merge
Option 3: Pick Specific Fields
const EmailUpdateSchema = UserSchema.pick({ email: true });
// Validate just email
const validatedChange = EmailUpdateSchema.parse(changes);
// Merge manually
const final = { ...original, ...validatedChange };Zod validates subset, you handle merge
🎯 Zod's Transform Philosophy
Transforms Apply to Input, Not Merge
const schema = z.object({
email: z.string()
.transform(s => s.toLowerCase())
.transform(s => s.trim())
});
// Transform applies to what you give it
const result = schema.parse({ email: ' USER@EXAMPLE.COM ' });
// → { email: 'user@example.com' }
// But if you're merging:
const original = { email: 'old@example.com' };
const changes = { email: ' NEW@EXAMPLE.COM ' };
// You must merge first
const merged = { ...original, ...changes };
const result = schema.parse(merged);
// → { email: 'new@example.com' }Key: Zod transforms INPUT data, not the merging process
💡 Real-World Pattern (Outside Zod)
Typical Flow:
// 1. Fetch original
const original = await db.get('CUST-001');
// 2. Define update schema (partial)
const UpdateSchema = UserSchema.partial();
// 3. Validate changes only
const validatedChanges = UpdateSchema.parse(userInput);
// 4. Merge manually
const merged = { ...original, ...validatedChanges };
// 5. Validate complete object
const final = UserSchema.parse(merged);
// 6. Save
await db.update('CUST-001', final);Zod used twice:
- Once for partial (changes)
- Once for complete (merged)
🔄 Your System vs Zod
Your Current Approach:
// 1. Fetch original (in controller)
const original = run_doc.output.data[0];
// 2. User provides changes
const delta = run_doc.input; // { email: 'new@example.com' }
// 3. Merge in controller
const merged = { ...original, ...delta };
// 4. Validate merged
const validation = controller._validateSchema(merged, schema);
// 5. Process/transform
const processed = await coworker.processDocument(run_doc);
// 6. Handler uses processedYour merge happens BEFORE validation/transformation
Zod Would Do:
// 1. Fetch original
const original = await fetch();
// 2. User provides changes
const delta = userInput;
// 3. Merge manually
const merged = { ...original, ...delta };
// 4. Parse (validate + transform)
const final = schema.parse(merged);
// 5. Use final
```
**Same pattern! Just different terminology**
---
## 🎯 **Key Insight: Zod Doesn't Do Merging**
### **What Zod Provides:**
```
Input → Validate → Transform → Output
```
### **What Zod Doesn't Provide:**
```
Original + Changes → Merge LogicYou must handle:
- Fetching original
- Merging original + changes
- Deciding merge strategy (shallow, deep, smart)
Zod handles:
- Validating the result
- Transforming the result
💡 Different Merge Strategies
1. Shallow Merge (Spread)
const merged = { ...original, ...changes };Problem: Replaces entire nested objects
2. Deep Merge
const merged = deepMerge(original, changes);Problem: May merge arrays unexpectedly
3. Field-by-Field (Smart Merge)
const merged = { ...original };
for (const [key, value] of Object.entries(changes)) {
if (value !== undefined) {
merged[key] = value;
}
}Problem: Doesn't handle nested objects
4. Schema-Driven Merge (Your Approach)
// Use schema to know how to merge each field type
for (const field of schema.fields) {
if (changes[field.fieldname] !== undefined) {
if (field.fieldtype === 'Table') {
// Smart merge for child tables
} else {
// Simple replace
merged[field.fieldname] = changes[field.fieldname];
}
}
}Best: Schema tells you how to merge each field
✅ What You Should Do
Your Controller Already Has the Right Pattern:
controller._processWrite(run_doc, opConfig) {
// 1. Fetch originals (if needed)
if (opConfig.fetchOriginals) {
const items = await fetchFromDB();
run_doc._items = items;
}
// 2. Get changes
const delta = run_doc.input;
// 3. Merge (schema-aware)
const merged = this._mergeData(original, delta, schema);
// 4. Validate merged
const validation = this._validateSchema(merged, schema);
// 5. Transform merged
const processed = await coworker.processDocument(run_doc);
// 6. Handler uses processed
}Add Explicit Merge Method:
controller._mergeData(original, changes, schema) {
const merged = { ...original };
for (const [fieldname, value] of Object.entries(changes)) {
const field = schema.fields.find(f => f.fieldname === fieldname);
if (!field) {
merged[fieldname] = value; // Unknown field, just set
continue;
}
// Schema-driven merge logic
if (field.fieldtype === 'Table') {
// Child table - smart merge
merged[fieldname] = this._mergeChildTable(
original[fieldname],
value
);
} else if (field.fieldtype === 'JSON') {
// JSON field - deep merge
merged[fieldname] = deepMerge(
original[fieldname],
value
);
} else {
// Simple fields - replace
merged[fieldname] = value;
}
}
return merged;
}🎯 Summary
How Zod Handles Editing Existing Data:
Answer: It doesn't.
Zod expects you to:
- ✅ Fetch original yourself
- ✅ Merge changes yourself
- ✅ Pass merged to Zod for parse
- ✅ Use Zod's output
Zod's job:
- Validate complete objects
- Transform data
- Return type-safe output
Your job:
- Fetch originals
- Merge strategy
- When to merge (before/after validation)
Your System Already Does This Better:
| Aspect | Zod | Your System |
|---|---|---|
| Merge logic | Manual (your code) | Schema-driven merge |
| Fetch originals | Manual | Controller handles (fetchOriginals config) |
| Merge strategy | Spread operator | Field-type aware |
| Transformation | During parse | 3-tier field processing |
| Validation | Before/after merge (your choice) | After merge, before transform |
Your architecture is actually MORE sophisticated than Zod for this use case because:
- Schema defines merge behavior per field type
- Controller orchestrates fetch + merge + validate + transform
- Field handlers know how to transform each type
- Everything is config-driven
Zod is great for parsing untrusted input. Your system is better for editing structured documents with complex field types.
The user is asking for best practices for their architecture where:
- Guardian (checks key parameters/state)
- Processor (validates + transforms)
- Work in pipeline with schemas
This is about defining the correct separation of concerns and flow between the guardian (controller state validation) and processor (data validation + transformation).
Let me think about best practices for this pattern:
- Guardian responsibilities:
- Check if operation is allowed (state validation)
- Check permissions
- Check docstatus
- Check read_only flags
- Early exit if blocked
- Fast, no data processing
- Processor responsibilities:
- Schema validation (field types, required, constraints)
- Data transformation (field handlers)
- Cross-field validation
- Business logic
- Works on the data itself
Key insights from the conversation:
- User has schema-driven architecture
- Frappe-style document lifecycle (docstatus)
- 3-tier field processing system
- Controller orchestrates the flow
- Need clear separation between "can I do this?" vs "is this data valid?"
Best practices should address:
- Clear separation of guardian vs processor
- When each runs
- What each checks
- How they interact with schema
- Performance (guardian is fast gate, processor is thorough)
- Error messages (different types from each)
- Caching schema
- Reusability
🎯 Best Practices: Guardian → Processor Pipeline
📊 Clear Separation of Concerns
Guardian (Fast Gate)
Purpose: "CAN this operation happen?"
Checks: State, permissions, locks
Speed: Very fast (no data processing)
Fails: Early, immediate return
Schema Use: Flags only (is_submittable, read_only)Processor (Thorough Validator)
Purpose: "IS this data valid?"
Checks: Data structure, types, constraints
Speed: Slower (processes all data)
Fails: Collects all errors
Schema Use: Full schema (fields, types, rules)✅ Best Practice 1: Guardian Checks Context, Not Content
Guardian Checks:
✓ doc.docstatus allows operation?
✓ schema.read_only blocks edits?
✓ field.read_only for specific field?
✓ User has permission?
✓ Document locked?
✓ Workflow state allows?
✗ NOT: Is email valid?
✗ NOT: Is value in range?
✗ NOT: Data type checkingProcessor Checks:
✓ Field types correct?
✓ Required fields present?
✓ Values in valid range?
✓ Email format valid?
✓ Cross-field rules?
✗ NOT: Permission checks
✗ NOT: Docstatus checks✅ Best Practice 2: Guardian Returns Reason, Processor Returns Errors
Guardian Response:
// Single reason, immediate fail
{
allowed: false,
reason: "Cannot edit submitted document",
code: "DOCSTATUS_SUBMITTED"
}Processor Response:
// Multiple errors, all collected
{
valid: false,
errors: [
{ field: 'email', message: 'Invalid email format' },
{ field: 'age', message: 'Must be >= 0' },
{ fields: ['start_date', 'end_date'], message: 'End must be after start' }
]
}✅ Best Practice 3: Schema Used Differently
Guardian Uses Schema Flags:
_guardianCheck(run_doc) {
const schema = run_doc.output?.schema;
const doc = run_doc.doc || run_doc.output?.data?.[0];
// Check flags only (fast)
if (schema.read_only) return { allowed: false, reason: '...' };
if (schema.is_submittable && doc.docstatus === 1) {
return { allowed: false, reason: '...' };
}
// Don't iterate fields here
return { allowed: true };
}Processor Uses Full Schema:
_processData(data, schema) {
const errors = [];
// Iterate all fields (thorough)
for (const field of schema.fields) {
const error = this._validateField(field, data[field.fieldname]);
if (error) errors.push(error);
}
return { valid: errors.length === 0, errors };
}✅ Best Practice 4: Guardian Runs First, Always
Pipeline Order:
controller.execute(run_doc) {
// 1. GUARDIAN (gate)
const guard = this._guardianCheck(run_doc);
if (!guard.allowed) {
return {
success: false,
error: { message: guard.reason, code: guard.code }
};
}
// 2. PROCESSOR (validate + transform)
const processed = await this._processData(run_doc);
if (!processed.valid) {
return {
success: false,
errors: processed.errors
};
}
// 3. HANDLER (execute)
return await handler(run_doc);
}Never skip guardian, even if you trust the data
✅ Best Practice 5: Guardian is Stateless, Processor Can Be Stateful
Guardian:
// Pure function, no side effects
_guardianCheck(run_doc) {
// Only reads, never writes
// No DB queries
// No state modification
return { allowed: true/false, reason };
}Processor:
// Can have side effects
_processData(run_doc) {
// Can fetch related data
// Can modify run_doc
// Can cache schema
// Can transform data
run_doc.input.data = transformed;
return { valid, errors };
}✅ Best Practice 6: Cache Schema, Don't Refetch
Pattern:
controller.execute(run_doc) {
// Load schema once
if (!run_doc.output?.schema) {
const schema = await this.getSchema(doctype);
run_doc.output = run_doc.output || {};
run_doc.output.schema = schema;
}
// Guardian uses cached schema
const guard = this._guardianCheck(run_doc);
// Processor uses same cached schema
const processed = this._processData(run_doc);
// Handler uses same cached schema
}Never fetch schema twice in same run
✅ Best Practice 7: Guardian Checks Can Be Composed
Composable Guardian:
_guardianCheck(run_doc) {
// Chain of checks
const checks = [
this._checkDocstatus,
this._checkReadOnly,
this._checkPermissions,
this._checkWorkflow,
this._checkLocks
];
for (const check of checks) {
const result = check(run_doc);
if (!result.allowed) {
return result; // Fail fast
}
}
return { allowed: true };
}
_checkDocstatus(run_doc) {
const doc = run_doc.doc;
const schema = run_doc.output?.schema;
const operation = run_doc.operation;
if (operation === 'update' && doc.docstatus === 2) {
return {
allowed: false,
reason: 'Cannot edit cancelled document',
code: 'DOCSTATUS_CANCELLED'
};
}
return { allowed: true };
}Each check is independent, testable
✅ Best Practice 8: Processor Works in Stages
Staged Processing:
_processData(run_doc) {
const data = run_doc.input?.data;
const schema = run_doc.output?.schema;
// Stage 1: Type validation
const typeCheck = this._validateTypes(data, schema);
if (!typeCheck.valid) return typeCheck;
// Stage 2: Schema rules
const schemaCheck = this._validateSchema(data, schema);
if (!schemaCheck.valid) return schemaCheck;
// Stage 3: Cross-field validation
const docCheck = this._validateDocument(data, schema);
if (!docCheck.valid) return docCheck;
// Stage 4: Transformation
const transformed = await this._transformData(data, schema);
run_doc.input.data = transformed;
return { valid: true };
}Ordered stages, can short-circuit
✅ Best Practice 9: Field-Level vs Document-Level
Guardian: Document-level only
_guardianCheck(run_doc) {
// Check document state
// Check document permissions
// Check document locks
// NOT field-specific checks
}Processor: Both levels
// Field-level (in loop)
for (const field of schema.fields) {
validateField(field, value);
}
// Document-level (after fields)
validateDocument(doc, schema);✅ Best Practice 10: Merge Happens Between Guardian and Processor
Pipeline with Merge:
controller.execute(run_doc) {
// 1. Guardian (checks original state)
const guard = this._guardianCheck(run_doc);
if (!guard.allowed) return error;
// 2. Fetch originals (if update)
if (needsOriginals) {
const originals = await fetch();
run_doc._originals = originals;
}
// 3. Merge (schema-driven)
if (run_doc._originals) {
const merged = this._mergeData(
run_doc._originals[0],
run_doc.input,
schema
);
run_doc.input.data = merged;
}
// 4. Processor (validates merged)
const processed = this._processData(run_doc);
if (!processed.valid) return errors;
// 5. Handler
return await handler(run_doc);
}Merge after guardian, before processor
✅ Best Practice 11: Guardian Check Can Be Field-Specific
Field-Level Guardian:
_guardianCheck(run_doc) {
// Document-level checks
// ...
// If operation updates single field
if (run_doc.operation === 'update' && isSingleFieldUpdate(run_doc)) {
const fieldname = Object.keys(run_doc.input)[0];
const field = schema.fields.find(f => f.fieldname === fieldname);
// Check field-specific rules
if (field.read_only) {
return {
allowed: false,
reason: `Field ${field.label} is read-only`
};
}
if (doc.docstatus === 1 && !field.allow_on_submit) {
return {
allowed: false,
reason: `Cannot edit ${field.label} after submission`
};
}
}
return { allowed: true };
}Guardian can check field-level flags, but not validate field values
✅ Best Practice 12: Different Error Types
Guardian Errors (Blocking):
{
success: false,
error: {
type: 'BLOCKED',
code: 'DOCSTATUS_SUBMITTED',
message: 'Cannot edit submitted document',
// No field-specific info
}
}Processor Errors (Validation):
{
success: false,
error: {
type: 'VALIDATION',
errors: [
{ field: 'email', message: '...', code: 'INVALID_EMAIL' },
{ field: 'age', message: '...', code: 'OUT_OF_RANGE' }
]
}
}Different error structures for different purposes
📋 Complete Best Practice Implementation
controller.execute(run_doc) {
// ════════════════════════════════════════════
// PHASE 1: SETUP
// ════════════════════════════════════════════
// Load schema (cache)
if (requiresSchema && !run_doc.output?.schema) {
run_doc.output = run_doc.output || {};
run_doc.output.schema = await this.getSchema(doctype);
}
// ════════════════════════════════════════════
// PHASE 2: GUARDIAN (Fast Gate)
// ════════════════════════════════════════════
const guard = this._guardianCheck(run_doc);
if (!guard.allowed) {
return {
success: false,
error: {
type: 'BLOCKED',
code: guard.code,
message: guard.reason
}
};
}
// ════════════════════════════════════════════
// PHASE 3: FETCH & MERGE (If Write)
// ════════════════════════════════════════════
if (isWriteOperation && fetchOriginals) {
const originals = await fetchFromDB();
if (originals.length > 0) {
const merged = this._mergeData(
originals[0],
run_doc.input,
schema
);
run_doc.input.data = merged;
run_doc._originals = originals;
}
}
// ════════════════════════════════════════════
// PHASE 4: PROCESSOR (Thorough Validation)
// ════════════════════════════════════════════
if (shouldValidate) {
const validation = this._processData(run_doc);
if (!validation.valid) {
return {
success: false,
error: {
type: 'VALIDATION',
errors: validation.errors
}
};
}
}
// ════════════════════════════════════════════
// PHASE 5: HANDLER
// ════════════════════════════════════════════
return await handler(run_doc);
}🎯 Summary: Guardian vs Processor
| Aspect | Guardian | Processor |
|---|---|---|
| Purpose | Can operation happen? | Is data valid? |
| Speed | Very fast | Slower |
| Checks | State, permissions | Data structure, values |
| Schema use | Flags only | Full schema |
| Fails | First error, immediate | All errors, collected |
| Side effects | None (pure) | Can transform |
| When | Before everything | After guardian, before handler |
| Error type | BLOCKED | VALIDATION |
Guardian guards the gate. Processor ensures quality. Both use schema, differently.
The user wants me to help create a config-based decision tree for the guardian checks. They want:
- A config variable that defines the decision tree
- Order of checks (doctype presence, schema existence, etc.)
- Frappe-like docstatus + is_submittable checks
- Field-level checks like read_only
They want this as a CONFIG, not code. A declarative decision tree.
Let me think about the structure:
coworker._config.guardianRules = {
// Order matters - these run in sequence
checks: [
{
id: 'doctype_present',
condition: 'run_doc has doctype',
errorCode: 'NO_DOCTYPE',
errorMessage: 'Doctype is required'
},
{
id: 'schema_loaded',
condition: 'schema exists',
errorCode: 'NO_SCHEMA',
errorMessage: 'Schema not found'
},
// etc
],
// Docstatus rules based on is_submittable
docstatusRules: {
'is_submittable=1': {
0: { // Draft
allow: ['update', 'submit', 'delete'],
block: []
},
1: { // Submitted
allow: ['cancel'],
block: ['update', 'delete'],
allowIfField: 'allow_on_submit' // Exception
},
2: { // Cancelled
allow: ['amend'],
block: ['update', 'delete', 'submit']
}
},
'is_submittable=0': {
// No docstatus restrictions
'*': {
allow: ['update', 'delete']
}
}
},
// Field-level rules
fieldRules: {
'field.read_only=1': {
allow: [],
block: ['update']
},
'field.allow_on_submit=1': {
// Exception: can edit even if docstatus=1
allow: ['update']
}
}
}I need to refine this structure to be clearer and more maintainable.
📋 Guardian Decision Tree Config
coworker._config.guardianRules = {
// ════════════════════════════════════════════════════════
// PHASE 1: PREREQUISITE CHECKS (Order matters)
// ════════════════════════════════════════════════════════
prerequisites: [
{
id: 'operation_exists',
check: (run_doc) => !!run_doc.operation,
failCode: 'NO_OPERATION',
failMessage: 'Operation is required'
},
{
id: 'doctype_exists',
check: (run_doc) => !!(run_doc.source_doctype || run_doc.target_doctype),
failCode: 'NO_DOCTYPE',
failMessage: 'Doctype is required',
requiredFor: ['update', 'delete', 'submit', 'cancel']
},
{
id: 'schema_loaded',
check: (run_doc) => !!run_doc.output?.schema,
failCode: 'NO_SCHEMA',
failMessage: 'Schema not loaded',
requiredFor: ['update', 'delete', 'submit', 'cancel']
},
{
id: 'document_exists',
check: (run_doc) => !!(run_doc.doc || run_doc.output?.data?.[0]),
failCode: 'NO_DOCUMENT',
failMessage: 'Document not found',
requiredFor: ['update', 'delete', 'submit', 'cancel']
}
],
// ════════════════════════════════════════════════════════
// PHASE 2: SCHEMA-LEVEL CHECKS (Frappe-like)
// ════════════════════════════════════════════════════════
schemaRules: {
// Check schema.read_only flag
read_only: {
check: (run_doc) => run_doc.output?.schema?.read_only === 1,
blockedOperations: ['update', 'delete'],
failCode: 'DOCTYPE_READONLY',
failMessage: 'This doctype is read-only'
},
// Check schema.is_submittable with docstatus
submittable: {
// When is_submittable = 1 (like Frappe)
enabled: {
check: (run_doc) => run_doc.output?.schema?.is_submittable === 1,
// Docstatus-based rules (EXACTLY like Frappe)
docstatusRules: {
// docstatus = 0 (Draft)
0: {
allow: ['update', 'submit', 'delete'],
block: ['cancel', 'amend'],
failMessage: 'Document is in draft state'
},
// docstatus = 1 (Submitted)
1: {
allow: ['cancel'],
block: ['update', 'delete', 'submit'],
failMessage: 'Document is submitted',
exceptions: {
// Exception: fields with allow_on_submit can be edited
'allow_on_submit': {
allow: ['update']
}
}
},
// docstatus = 2 (Cancelled)
2: {
allow: ['amend'],
block: ['update', 'delete', 'submit', 'cancel'],
failMessage: 'Document is cancelled'
}
}
},
// When is_submittable = 0 (no workflow restrictions)
disabled: {
check: (run_doc) => run_doc.output?.schema?.is_submittable !== 1,
// No docstatus restrictions - all operations allowed by schema
}
}
},
// ════════════════════════════════════════════════════════
// PHASE 3: FIELD-LEVEL CHECKS
// ════════════════════════════════════════════════════════
fieldRules: {
// When updating specific field
applicableWhen: (run_doc) => {
// Only for single-field updates
return run_doc.operation === 'update' &&
run_doc.input &&
Object.keys(run_doc.input).length === 1;
},
checks: [
{
id: 'field_read_only',
check: (field, doc, schema) => field.read_only === 1,
failCode: 'FIELD_READONLY',
failMessage: (field) => `${field.label || field.fieldname} is read-only`
},
{
id: 'field_read_only_depends_on',
check: (field, doc, schema) => {
if (!field.read_only_depends_on) return false;
return evaluateDependsOn(field.read_only_depends_on, doc);
},
failCode: 'FIELD_READONLY_CONDITIONAL',
failMessage: (field) => `${field.label || field.fieldname} is read-only in current state`
},
{
id: 'field_allow_on_submit',
check: (field, doc, schema) => {
// Check if field can be edited after submission
if (schema.is_submittable !== 1) return false; // Not submittable, N/A
if (doc.docstatus !== 1) return false; // Not submitted, N/A
return field.allow_on_submit !== 1; // If NOT allow_on_submit, then blocked
},
failCode: 'FIELD_NO_SUBMIT_EDIT',
failMessage: (field) => `Cannot edit ${field.label || field.fieldname} after submission`
}
]
},
// ════════════════════════════════════════════════════════
// PHASE 4: PERMISSION CHECKS (Placeholder)
// ════════════════════════════════════════════════════════
permissionRules: {
enabled: false, // Set to true when implementing permissions
checks: [
{
id: 'user_permission',
check: (run_doc, user) => {
// Check user has permission for operation on doctype
return true; // Placeholder
},
failCode: 'NO_PERMISSION',
failMessage: 'You do not have permission for this operation'
}
]
}
};🎯 Usage Pattern
controller._guardianCheck(run_doc) {
const rules = coworker._config.guardianRules;
// ════════════════════════════════════════════
// PHASE 1: Prerequisites
// ════════════════════════════════════════════
for (const prereq of rules.prerequisites) {
// Check if required for this operation
if (prereq.requiredFor &&
!prereq.requiredFor.includes(run_doc.operation)) {
continue; // Skip this check
}
// Run check
if (!prereq.check(run_doc)) {
return {
allowed: false,
reason: prereq.failMessage,
code: prereq.failCode,
phase: 'prerequisite',
checkId: prereq.id
};
}
}
// ════════════════════════════════════════════
// PHASE 2: Schema Rules
// ════════════════════════════════════════════
const schema = run_doc.output?.schema;
const doc = run_doc.doc || run_doc.output?.data?.[0];
const operation = run_doc.operation;
// Check read_only
if (rules.schemaRules.read_only.check(run_doc)) {
if (rules.schemaRules.read_only.blockedOperations.includes(operation)) {
return {
allowed: false,
reason: rules.schemaRules.read_only.failMessage,
code: rules.schemaRules.read_only.failCode,
phase: 'schema',
checkId: 'read_only'
};
}
}
// Check submittable + docstatus
if (rules.schemaRules.submittable.enabled.check(run_doc)) {
const docstatus = doc.docstatus;
const statusRules = rules.schemaRules.submittable.enabled.docstatusRules[docstatus];
if (statusRules) {
// Check if operation is blocked
if (statusRules.block.includes(operation)) {
// Check exceptions
if (statusRules.exceptions?.allow_on_submit) {
// Check if this is single field update with allow_on_submit
const fieldname = Object.keys(run_doc.input || {})[0];
if (fieldname) {
const field = schema.fields.find(f => f.fieldname === fieldname);
if (field?.allow_on_submit === 1) {
// Exception applies - allow
} else {
return {
allowed: false,
reason: statusRules.failMessage,
code: 'DOCSTATUS_BLOCK',
phase: 'schema',
checkId: 'submittable_docstatus',
docstatus: docstatus
};
}
}
} else {
return {
allowed: false,
reason: statusRules.failMessage,
code: 'DOCSTATUS_BLOCK',
phase: 'schema',
checkId: 'submittable_docstatus',
docstatus: docstatus
};
}
}
// Check if operation is allowed
if (!statusRules.allow.includes(operation)) {
return {
allowed: false,
reason: `Operation ${operation} not allowed for ${statusRules.failMessage}`,
code: 'OPERATION_NOT_ALLOWED',
phase: 'schema',
checkId: 'submittable_docstatus',
docstatus: docstatus
};
}
}
}
// ════════════════════════════════════════════
// PHASE 3: Field Rules
// ════════════════════════════════════════════
if (rules.fieldRules.applicableWhen(run_doc)) {
const fieldname = Object.keys(run_doc.input)[0];
const field = schema.fields.find(f => f.fieldname === fieldname);
if (field) {
for (const fieldCheck of rules.fieldRules.checks) {
if (fieldCheck.check(field, doc, schema)) {
return {
allowed: false,
reason: fieldCheck.failMessage(field),
code: fieldCheck.failCode,
phase: 'field',
checkId: fieldCheck.id,
field: fieldname
};
}
}
}
}
// ════════════════════════════════════════════
// PHASE 4: Permissions (if enabled)
// ════════════════════════════════════════════
if (rules.permissionRules.enabled) {
// Check permissions...
}
// ════════════════════════════════════════════
// ALL CHECKS PASSED
// ════════════════════════════════════════════
return { allowed: true };
}✅ Key Features
| Feature | Benefit |
|---|---|
| Ordered prerequisites | Checks run in sequence, fail fast |
| Frappe-compatible | Exact same docstatus logic |
| Schema flags first | read_only, is_submittable checked |
| Field-level granular | read_only, allow_on_submit per field |
| Declarative config | All rules in one place |
| Extensible | Add new checks without changing code |
| Debuggable | Returns which check failed and why |
This config-driven approach lets you see the entire decision tree in one place and modify rules without touching the guardian logic.
The user is asking me to evaluate if their proposed config-driven guardian decision tree is good. Let me assess it critically:
Strengths:
- Very comprehensive - covers prerequisites, schema, field, and permission levels
- Frappe-compatible - mimics Frappe's exact docstatus logic
- Ordered execution - clear phase progression
- Declarative - all rules in config, not scattered in code
- Extensible - easy to add new rules
- Detailed error responses - includes phase, checkId, specific context
Potential Issues:
- Complexity - might be over-engineered for initial implementation
- Performance - lots of checks on every operation
- Function in config - some checks are functions, not pure data (maintainability concern)
- Nested structure - deep nesting could be hard to navigate
- Field-level checks mixed with document-level - might be doing too much in guardian
- Exception handling complexity - the allow_on_submit exception logic is nested deeply
What's Actually Good:
- The CONCEPT is excellent
- The phases are well thought out
- The Frappe compatibility is important
- The ordering (prerequisites → schema → field → permissions) is correct
What Could Be Simpler:
- Phase 1 (prerequisites) could be simpler - just check existence
- Phase 2 (schema) is the core - this is good
- Phase 3 (field rules) might be over-engineered for guardian - could be in processor
- Phase 4 (permissions) placeholder is fine
My honest assessment:
This is a GOOD design conceptually, but might be:
- Too complex for initial implementation
- Mixing concerns (some field checks might belong in processor)
- Functions in config reduce pure declarative benefit
I should give balanced feedback: what's good, what's concerning, and suggest simplification without discouraging the good architectural thinking.
🎯 Honest Assessment
✅ What's EXCELLENT
1. The Phased Approach
Prerequisites → Schema → Field → PermissionsPerfect order. This is exactly right.
2. Frappe-Compatible Docstatus Logic
docstatusRules: {
0: { allow: ['update', 'submit', 'delete'] },
1: { allow: ['cancel'] },
2: { allow: ['amend'] }
}This is exactly how Frappe works. Critical to get right.
3. Explicit Prerequisites
prerequisites: [
'operation_exists',
'doctype_exists',
'schema_loaded',
'document_exists'
]Good fail-fast checks. Clean separation.
4. Detailed Error Context
return {
allowed: false,
reason: '...',
code: '...',
phase: 'schema',
checkId: 'submittable_docstatus',
docstatus: 0
}Excellent for debugging. Know exactly which rule failed.
⚠️ Concerns
1. Too Complex for Initial Implementation
Problem: This is a LOT of config to write and maintain upfront.
Reality Check:
- 90% of your checks will be: "is docstatus 0?" and "is field read_only?"
- The complex nested exception logic might never be used
Suggestion: Start simpler, add complexity when needed.
2. Functions in Config Reduce Declarative Benefits
check: (run_doc) => !!run_doc.operationProblem:
- Can't serialize to JSON
- Can't inspect without executing
- Harder to test independently
- Not truly "declarative"
Alternative: Pure data structure with separate check functions:
prerequisites: [
{ id: 'operation_exists', path: 'operation', required: true }
]
// Check function separate
checkPrerequisite(rule, run_doc) {
return !!getPath(run_doc, rule.path);
}3. Field-Level Checks Might Belong in Processor
fieldRules: {
checks: [
'field_read_only',
'field_read_only_depends_on',
'field_allow_on_submit'
]
}Question: Are these guardian checks or validation checks?
Guardian should check: Can operation happen at document level? Processor should check: Is this specific field value valid?
Field read_only might be guardian, but depends_on evaluation feels like processor work.
4. Deep Nesting is Hard to Navigate
schemaRules: {
submittable: {
enabled: {
docstatusRules: {
1: {
exceptions: {
'allow_on_submit': { ... }
}
}
}
}
}
}5 levels deep! Hard to find what you need.
5. Exception Logic is Complex
The allow_on_submit exception handling in the check code:
if (statusRules.exceptions?.allow_on_submit) {
const fieldname = Object.keys(run_doc.input || {})[0];
if (fieldname) {
const field = schema.fields.find(f => f.fieldname === fieldname);
if (field?.allow_on_submit === 1) {
// Exception applies
} else {
return blocked;
}
}
}This is business logic in the check code, not config.
💡 Simplified Alternative
Start With This (80% of what you need):
coworker._config.guardianRules = {
// Simple boolean checks
schemaBlocks: {
'read_only': {
flag: 'read_only',
value: 1,
blocks: ['update', 'delete'],
message: 'Doctype is read-only'
}
},
// Frappe docstatus matrix (pure data)
docstatusMatrix: {
// When is_submittable = 1
0: ['update', 'submit', 'delete'], // Draft
1: ['cancel'], // Submitted
2: ['amend'] // Cancelled
},
// Simple field flags
fieldBlocks: {
'read_only': {
flag: 'read_only',
value: 1,
message: (field) => `${field.label} is read-only`
},
'no_submit_edit': {
flag: 'allow_on_submit',
value: 0, // Not allowed
when: { docstatus: 1 },
message: (field) => `Cannot edit ${field.label} after submission`
}
}
};Then check code is simple:
_guardianCheck(run_doc) {
// 1. Check schema read_only
if (schema.read_only === 1) {
if (['update', 'delete'].includes(operation)) {
return blocked('Doctype is read-only');
}
}
// 2. Check docstatus (if submittable)
if (schema.is_submittable === 1) {
const allowedOps = docstatusMatrix[doc.docstatus] || [];
if (!allowedOps.includes(operation)) {
return blocked(`Cannot ${operation} document with docstatus ${doc.docstatus}`);
}
}
// 3. Check field (if single field update)
if (isSingleFieldUpdate) {
if (field.read_only === 1) {
return blocked(`${field.label} is read-only`);
}
if (doc.docstatus === 1 && field.allow_on_submit !== 1) {
return blocked(`Cannot edit ${field.label} after submission`);
}
}
return { allowed: true };
}🎯 My Recommendation
Phase 1: Start Simple (Now)
guardianRules: {
// Just the essential Frappe checks
docstatusMatrix: { 0: [...], 1: [...], 2: [...] },
fieldFlags: ['read_only', 'allow_on_submit']
}
// Simple check code (50 lines)Phase 2: Add When Needed (Later)
- Prerequisites config (if you need validation)
- Permission rules (when you implement permissions)
- Complex exceptions (when you encounter them)
✅ Final Answer: Is It Good?
Conceptually: ⭐⭐⭐⭐⭐ (5/5)
- Excellent architecture
- Right phases
- Correct Frappe logic
- Well thought out
Practically: ⭐⭐⭐ (3/5)
- Too complex for initial implementation
- Functions in config reduce declarative benefit
- Deep nesting hard to navigate
- Mixing guardian/processor concerns
Recommendation:
Start with 20% of this (the core Frappe docstatus checks) and grow it as you discover what you actually need.
Your current comprehensive config is EXCELLENT documentation of what checks COULD exist, but implement incrementally:
- ✅ Implement docstatus matrix first (most critical)
- ✅ Add field-level checks second (read_only, allow_on_submit)
- ⏳ Add prerequisites if needed (probably not)
- ⏳ Add complex exceptions as you encounter them
The design is good. The implementation should be gradual.
Does this make sense?